The ferlin gene family possesses a rare and identifying feature consisting of multiple tandem C2 domains and a C-terminal transmembrane domain. Much currently remains unknown about the fundamental function of this gene family, however, mutations in its two most well-characterised members, dysferlin and otoferlin, have been implicated in human disease. The availability of genome sequences from a wide range of species makes it possible to explore the evolution of the ferlin family, providing contextual insight into characteristic features that define the ferlin gene family in its present form in humans.C. elegans and D. melanogaster, present as outgroups in the phylogenetic analysis, with results suggesting reproduction-related divergence and specialization of species-specific functions within their genus.Ferlin genes were detected from all species of representative phyla, with two ferlin subgroups partitioned within the ferlin phylogenetic tree based on the presence or absence of a DysF domain. Invertebrates generally possessed two ferlin genes (one with DysF and one without), with six ferlin genes in most vertebrates . Expansion of the ferlin gene family is evident between the divergence of lamprey (jawless vertebrates) and shark (cartilaginous fish). Common to almost all ferlins is an N-terminal C2-FerI-C2 sandwich, a FerB motif, and two C-terminal C2 domains (C2E and C2F) adjacent to the transmembrane domain. Preservation of these structural elements throughout eukaryotic evolution suggests a fundamental role of these motifs for ferlin function. In contrast, DysF, C2DE, and FerA are optional, giving rise to subtle differences in domain topologies of ferlin genes. Despite conservation of multiple C2 domains in all ferlins, the C-terminal C2 domains (C2E and C2F) displayed higher sequence conservation and greater conservation of putative calcium binding residues across paralogs and orthologs. Interestingly, the two most studied non-mammalian ferlins (Fer-1 and Misfire) in model organisms Our phylogenetic studies provide evolutionary insight into the ferlin gene family. We highlight the existence of ferlin-like proteins throughout eukaryotic evolution, from unicellular phytoplankton and apicomplexan parasites, through to humans. We characterise the preservation of ferlin structural motifs, not only of C2 domains, but also the more poorly characterised ferlin-specific motifs representing the DysF, FerA and FerB domains. Our data suggest an ancient role of ferlin proteins, with lessons from vertebrate biology and human disease suggesting a role relating to vesicle fusion and plasma membrane specialization. Caenorhabditis elegans spermatogenesis factor Fer-1  (East Coast fever), Babesia [Genbank:XP_001610088] (tick fever) and Toxoplasma [Genbank:XP_002364209] (toxoplasmosis) (data not shown), that account for significant worldwide mortality and morbidity amongst humans and livestock. Similar to the metazoan ferlins, the FerI domains in these Apicomplexan parasites are sandwiched closely between two C2 domains. This could suggest that the C2-FerI-C2 motif functions as a single entity and may have a fundamental function shared between metazoan and protozoan putative ferlins.The FerI and DysF domains are of particular interest as both have been identified in ferlin-like proteins of unicellular eukaryotes. The FerI domain, but not the DysF domain, was discovered in ferlin-like proteins of protozoan Apicomplexan parasites Plasmodium , Theileria [Genbank:NP_056210] which contains two DysF domains, but not present in the embedded form, as is the case with the ferlins. A family of yeast peroxisomal proteins Pex30p, Pex31p and Pex32p has also been identified to contain a DysF domain, again as a non-embedded form [Figures ded form . StudiesOver recent years, advances in sequencing technology have led to the increasing pursuit of genome-wide sequencing of many species. The public availability of data via online databases has in turn enabled individuals to pursue phylogenetic research of their gene of interest to complement their laboratory studies. In this study we present a phylogenetic study, selecting representative vertebrate and invertebrate eukaryotic genomes to shed evolutionary insight into the characteristic features that define the ferlin gene family.de novo in 10-20 seconds remains a curious area of research for many in the field [There are six ferlin genes identified in humans to date; three consisting of a DysF domain (Type 1), and three without (Type 2). However, our genomic analysis identified only two ferlins in invertebrates (one Type 1 and one Type 2), suggesting that the six mammalian ferlins originated from two ancestral ferlins of distinct subtypes. The need for metazoans to maintain ferlins of two different types suggests that DysF imparts a specific function, conserved throughout evolution. It is not clear whether the DysF domain was gained in an ancestral ferlin then maintained throughout evolution due to a selective advantage, or, whether the DysF domain was lost following a gene duplication event, also imparting a selective advantage. Arthropods and nematodes were observed as exceptions; a DysF-containing ferlin is not maintained in arthropods, while a non-DysF ferlin is not maintained in nematodes. Using highly conserved C2 domain sequences, we were able to identify and extract two ferlin paralogs in the lamprey (Cmil) and five in the shark (Pmar), narrowing down the likely expansion of the ferlin gene family between the divergence of the jawless vertebrates and the cartilaginous fish. Ferlin-like genes consisting of at least five C2 domains, a C-terminal transmembrane region, and a C2-FerI-C2 motif were also identified in five species of Apicomplexa parasites , and in unicellular phytoplankton (Ostreococcus), further supporting an ancient role of ferlin-like proteins in eukaryotic biology. Plasmodium and other apicomplexan parasites are characterised by a specialized apicoplast membrane, possess specialised secretory organelles (rhoptries) thought to be involved in events leading to host cell invasion, and form membrane vesicular structures termed 'parasitophorous vacuolar membrane' (PVM) in which the organism resides . Given the field . With emhe field ,6,10, anhe field , placenthe field , and spehe field , a potenC. elegans, Fer-1 has since been thought of as the ancestral ferlin from which the human ferlins were derived. Our phylogenetic analysis of multiple invertebrate ferlins suggests that Fer-1 is not a typical ferlin gene. Despite some regions of homology, Fer-1 (and Drosophila Misfire) form outgroups in the ferlin phylogenetic tree  may represent a useful model organism, possessing the full complement of both DysF and non-DysF subgroups, and also technical utility for studies of muscle form and function.Following the identification of the dysferlin gene in 1998, and the shared homology with Fer-1 of e Figure , which iIn our sequence analysis of ferlin genes, we identified several conserved features amongst ferlin proteins; multiple tandem C2 domains (5-7), a single C-terminal transmembrane domain, a FerB domain, and a highly conserved N-terminal motif consisting of a FerI tightly sandwiched between two C2 domains. Slight variations in topologies include the incorporation of the optional C2A domain, the C2DE domain, and the FerA domain that appears to be concurrently present with the DysF domain.Our analysis of the DysF domain shows it has maintained high sequence conservation. The presence of two DysF domains in a nested fashion is a unique and ancient feature of the ferlin family preserved from early diverging metazoans  through to humans. Despite resolution of the inner DysF domain structure of myoferlin, its function and purpose remains unknown . Our anaIn the species we have analyzed, the C-terminal C2 domains of the ferlins are shown to be more conserved than the N-terminal C2 domains. This suggests that the ferlin C-terminus is perhaps responsible for functions more fundamental, than specialized. In contrast, we observed more divergence within the N-terminus, whereby some species lack C2A, or possess a variation that is highly divergent. These data suggest the ferlin N-terminus could be responsible for functions that are more species specific, and/or ortholog specific, rather than unified across the ferlin family. Despite the lack of conservation of C2A and its absence in certain species, there is evidence to suggest that it remains functional in human ferlins. The solved structure of myoferlin C2A indicates that it is capable of folding into the characteristic C2 domain beta-sandwich, although consisting of more than the typical eight strands. In addition, an alternate splice isoform of C2A of unknown function, in which an alternate exon 1 is used in C2A, is reported to be expressed at significant levels in skeletal muscle and blood cells . Furtherindependent phospholipid binding associated with the C2 domains presented here as most likely to possess calcium sensitivity [As seen in the ferlins, several C2 domains belonging to the synaptotagmin family also show degeneration of calcium-binding residues. In synaptotagmins, where structural data in addition to sequence data is available, these changes have been shown to confer ablation of calcium sensitivity . Despitesitivity . It is hsitivity . In otofsitivity , which isitivity .The function and need for so many C2 domains in the ferlins remains a mystery. To draw further lessons from the synaptotagmin family, possible reasons could be attributed to preferential lipid recognition, differential calcium sensitivities, or an expanded repertoire of protein-protein interactions. When the crystal structure of the cytosolic portion of Synaptotagmin I was solved, it was discovered that C2A and C2B were facing in opposite directions . This peThe idea of synergy and co-operativity between tandem C2 domains is also another novel concept explored in synaptotagmins. Interestingly, studies of C2A and C2B in isolation show they display different properties than when the two are studied together in tandem. In isolation, C2A of Synaptotagmin I does not bind SNAREs, whereas C2B does not penetrate lipid membranes. However, when tethered to an adjacent C2 domain, C2A is able to bind SNAREs , whereasIn this study we have surveyed a range of domain topologies present amongst ferlin genes, highlighting highly preserved domain regions, as well as species-specific domain combinations. Together, this information provides valuable insight into the minimal components required to construct a basic ferlin-like gene. Research into mini-gene (truncated) therapeutic constructs for gene restoration is currently underway for other genes associated with muscular dystrophy such as dystrophin, whose size exceeds the 5kb AAV vector packaging limit . We hypoIn summary, we define ferlins as an ancient family of C2 domain-containing proteins that are likely to possess primordial functions in eukaryotic biology. Our studies demonstrate expansion of the ferlin family during vertebrate evolution; often a basis for functional specialization and tissue-specific expression. We provide useful contextual insight into evolutionary preservation of not only the C2 domains, but also the less studied DysF, FerI, FerA and FerB protein domains. Defining the roles of each of these domains is essential to delineate the biology of ferlins, with clinical relevance to inherited human disease (dysferlin and otoferlin), and perhaps more widely significant in future studies of apicomplexan parasitology.C.elegans (Fer-1) and Drosophila (misfire) presented as phylogenetic outgroups, suggesting reproduction-related divergence. All ferlins possess an N-terminal C2-FerI-C2 sandwich, a FerB motif, and two C-terminal C2 domains adjacent to an extreme C-terminal transmembrane domain. Preservation of these structural elements throughout eukaryotic evolution suggests a fundamental role of these motifs for ferlin function.In this study we show that ferlins are an ancient family of genes common across protists and metazoans, but not in plants or fungi. Metazoans were shown to maintain distinct Type I (with a Dysf domain) and Type II (without a Dysf domain) ferlin lineages; with invertebrates generally possessing two ferlins, and vertebrates possessing six ferlins. Unexpectedly, ferlins from model organisms SC and KN supervised the study. AL and ML conceived of the study and carried out the work. All authors read and approved the final manuscript.Maximum likelihood tree of ferlins from the Caenorhabditis genus.Click here for fileMaximum likelihood tree of ferlins (Misfire) from the Drosophila genus.Click here for file


Drosophila. Genomic studies, thus far, have analyzed each protein as a single entity. However, the targets of adaptive events may be localized to particular parts of proteins, such as protein domains or regions involved in protein folding. We compared the population genetic mechanisms driving sequence polymorphism and divergence in defined protein domains and non-domain regions. Interestingly, we find that non-domain regions of proteins are more frequent targets of directional selection. Protein domains are also evolving under directional selection, but appear to be under stronger purifying selection than non-domain regions. Non-domain regions of proteins clearly play a major role in adaptive protein evolution on a genomic scale and merit future investigations of their functional properties.Adaptive protein evolution is pervasive in  Population genetics analyses indicate that protein divergence in Drosophila, unlike in humans and Arabidopsis, is frequently adaptive Drosophila simulans genome project.Protein domains serve a diversity of specialized functions relating to biochemical activity, binding affinity, subcellular location, or other aspects of protein biology. Regions of proteins that are not annotated as belonging to a domain may still have critical, yet unknown roles in protein function. This parsing of proteins raises the question as to which portion of proteins, domain vs. non-domain is more often subject to directional selection. In one world-view, if adaptive evolution implies functional divergence, such divergence might be more likely to occur in a known, functional domain. Alternatively, if most adaptive protein evolution resulted from fine scale tuning of function relating to, for example, protein folding, then adaptation might tend to occur in non-domain regions. Importantly, rates of divergence in annotated versus unannotated regions of proteins do not resolve these issues because variation in functional constraint cannot be distinguished from variation in the frequency of directional selection. We set out to investigate these issues on a whole-genome scale using population genetic data from the D. simulans lines was aligned to the reference sequence from the closely related species, D. melanogasterD. yakuba, to D. melanogaster was used to infer substitutions specifically along the D. simulans lineage. Thus, the rich annotation of D. melanogaster was used directly to investigate polymorphism and divergence in D. simulans. Drosophila melanogaster annotations define the locations of many functional protein domains. We directly superimposed PROSITE domain coordinates . In both cases, the ratio of synonymous to nonsynonymous fixations is smaller than the corresponding ratio for polymorphism, which is consistent with adaptive protein divergence. Polymorphic and fixed synonymous variants in non-domain vs. domain sites are not significantly heterogeneous  indicating more frequent adaptive evolution in non-domain regions, which is consistent with the interpretation of the MK tests on pooled domain and non-domain codons. However, the proportion of codons in domains is much lower than in non-domain regions  and rates of amino acid divergence are slower. These two factors lead to many fewer counts being recorded in protein domains. Additionally, the method used to calculate NI , which is simply a different arrangement of McDonald-Kreitman 2×2 contingency tables e NI see  is partiIn summary, both protein domains and amino acids in non-domain regions have experienced a high proportion of adaptive substitutions. Interestingly, non-domain regions appear to experience more frequent bouts of directional selection. This suggests that although non-domain regions may be less attractive targets of functional analysis in the laboratory, they are extremely important in terms of functional divergence under selection in nature. Future investigations of the mechanistic explanation of frequent adaptive evolution in non-domain regions, whether it is due to fine-tuning of folding patterns or yet to be discovered functions of non-domain regions, are clearly warranted.D. melanogaster v4.3 annotation were retrieved by querying the ensembl database PROSITE protein domain coordinates from the D. simulans and D. yakuba to the D. melanogaster reference are from D. melanogaster v4.3 annotation from Flybase (ftp://ftp.flybase.net/genomes/Drosophila_melanogaster/dmel_r4.3_20060303/fasta). A single isoform from each gene (i.e. the isoform with the greatest number of codons) was used for analyses. We used a conservative set of genes that preserved the gene model of D. melanogaster in both D. simulans and D. yakuba. More specifically, the start codon and splice junction locations and sequence and the termination codon location agreed with the D. melanogaster reference sequence.Syntenic alignments of  w = π * (4/3)] under the assumption that males and females have equal population sizes. Lineage-specific divergence was estimated by maximum likelihood using PAML v3.14 D. simulans line with greater than 20 codons in the segment being analyzed. PAML was run in batch mode using a BioPerl wrapper Polymorphism, as measured by nucleotide diversity (π), was estimated as in D. melanogaster and D. yakuba were identical. This allowed us to attribute fixed differences to the D. simulans lineage. Counts of nonsynonymous and synonymous polymorphisms and diverged sites took the path that minimized the number of nonsynonymous substitutions. All data were included in genomic comparisons of domains vs. non-domains. To be included in gene-by-gene domain vs. non-domain NI analyses, we required that there be at least 5 nonsynonymous variants and 5 synonymous variants for each domain/non-domain region. The neutrality index was calculated as the ratio of nonsynonymous polymorphisms to fixations divided by the ratio of synonymous polymorphisms to fixations For counts of polymorphic and fixed differences, we only analyzed codons where Table S1PROSITE domain coordinates.(0.47 MB TXT)Click here for additional data file.Table S2Counts of polymorphic and fixed sites for (1) the portion of each gene in protein domains and (2) the remainder of the protein for each gene.(0.39 MB TXT)Click here for additional data file.Table S3Estimates of polymorphism and divergence for (1) the portion of each gene in protein domains and (2) the remainder of the protein for each gene.(1.10 MB TXT)Click here for additional data file.


A genome-scale specificity and interaction map for yeast SH3 domain-containing proteins reveal how family members show selective binding to target proteins and predicts the dynamic localization of new candidate endocytosis proteins. Saccharomyces cerevisiae. Although most of the SH3 domains fall into the canonical classes I and II, each domain utilizes distinct features of its cognate ligands to achieve binding selectivity. Furthermore, we uncovered several SH3 domains with specificity profiles that clearly deviate from the two canonical classes. In conjunction with phage display, we used yeast two-hybrid and peptide array screening to independently identify SH3 domain binding partners. The results from the three complementary techniques were integrated using a Bayesian algorithm to generate a high-confidence yeast SH3 domain interaction map. The interaction map was enriched for proteins involved in endocytosis, revealing a set of SH3-mediated interactions that underlie formation of protein complexes essential to this biological pathway. We used the SH3 domain interaction network to predict the dynamic localization of several previously uncharacterized endocytic proteins, and our analysis suggests a novel role for the SH3 domains of Lsb3p and Lsb4p as hubs that recruit and assemble several endocytic complexes.SH3 domains are peptide recognition modules that mediate the assembly of diverse biological complexes. We scanned billions of phage-displayed peptides to map the binding specificities of the SH3 domain family in the budding yeast,  Saccharomyces cerevisiae, using a range of experimental methods. We found that although most of the intrinsic binding specificity for SH3 domains can be summarized by the two previously described canonical binding modes, each individual SH3 domain that we studied utilizes unique features of its cognate ligand to achieve binding selectivity. Moreover, some domains exhibit binding specificities that are distinct from the two canonical classes. We integrated peptide-SH3 domain binding data from three complementary screening techniques using a Bayesian statistical model to generate a protein–protein interaction network for the budding yeast SH3 domain family. This network was highly enriched in endocytosis proteins and their interactions. By examining these interactions in detail, we show that our SH3 domain network can be used to predict the temporal localization of several previously uncharacterized proteins to dynamic complexes that orchestrate the process of endocytosis.Significant diversity exists in protein structure and function, yet certain structural domains are used repeatedly across species to execute similar functions. The SH3 domain is one such common structural domain. It is found in signaling proteins and mediates protein–protein interactions by binding to short peptide sequences generally composed of proline. To investigate both the generality and selectivity of peptide binding by SH3 domains, we examined peptide specificity for almost all SH3 domains encoded within the proteome of the budding yeast,  Families of peptide recognition modules (PRMs), such as PDZ (PSD-95/Discs-large/ZO-1), SH2 (Src homology 2), and SH3 (Src homology 3) domains bind peptide motifs within proteins to mediate protein–protein interactions required for the assembly of stable or transient biological complexes In general, PRMs engage in protein–protein interactions by recognizing a core motif common to a domain family as well as additional ligand features that are more specific for each family member as is the case for PDZ domains A general genome-wide analysis of PRMs would involve defining all the domains from their primary sequence, profiling their ligand-binding specificities in detail, predicting natural ligands for each domain, and mapping large-scale protein–protein interaction networks for each domain family. Here, we present the first high-resolution analysis of the yeast SH3 domain family. First, we used large-scale phage-displayed peptide libraries and extensive sequencing to generate high-resolution binding profiles, which we show accurately represent binding specificity across multiple SH3 domain ligand positions. Second, we used the resulting specificity profiles to identify putative interactions in the yeast proteome, which were subsequently confirmed using oriented synthetic peptide arrays. Third, we conducted large-scale yeast two-hybrid screens to generate a physical interaction network mediated by the set of yeast SH3 domains. Finally, the datasets were integrated using Bayesian networks to generate a global SH3 domain interaction map in yeast.Applying the integrated probabilistic network revealed an intricate array of SH3-mediated interactions amongst proteins that make up the endocytic machinery. Investigation and comparison of the dynamics of protein localization within this network showed that the modular network predictions of the spatiotemporal dynamics of several novel endocytotic components were correct. In particular, our analysis predicts that the SH3 domains from Lsb3p and Lsb4p interact with multiple endocytic proteins and therefore might act as hubs to cluster these proteins at sites of endocytosis.S-transferase (GST) and purified 24 out of 27 fusion proteins in a stable, soluble form. For two of the three recalcitrant domains, the C-terminal domain of Bem1p (Bem1-2) and the Bud14p domain, we extended the sequence boundaries by examining the conservation of the domain regions across diverse fungal species. Based on this analysis, the domain boundaries for these two SH3 domains were extended, enabling the isolation of stable GST fusion proteins  exhibit dual specificity for both ligand classes . FurtherSP) score, which was applied previously to the PDZ domain family SP value summarizes the specificity observed in each column of a PWM as a numerical value ranging from zero (least specific) to one  to calculate reliable SP scores for 26 distinct specificity profiles. By summing the SP score across all PWM columns, we calculated a total SP (tSP) score for each SH3 domain specificity profile. Most yeast SH3 domains exhibit similar intrinsic specificities with tSP values ranging from four to six  derived from previous in vitro binding assays with synthetic peptides r2 = 0.97; p = 7.8×10−5; r2 = 0.73; p = 2.1×10−4; r2>0.8). Notably, 22 of our SH3 domain specificity profiles are derived from 30 or more ligands, suggesting that the majority of our phage-derived data can predict accurately the relative in vitro affinities of peptide ligands for SH3 domains.Phage display generally selects high-affinity ligands through an iterative panning process, and high-resolution PWMs have been used to predict preferences in selectivity that reflect binding affinities for PDZ domain–ligand interactions The use of synthesized peptide arrays offers an alternative and independent approach to query PRM–ligand interactions. In an ideal scenario, unique peptides representing the entire proteome of the organism would be spotted onto an array and assayed individually for interactions with a PRM of interest. However, in practice, a filtering step is required to generate an array of manageable size. In a strategy dubbed WISE (whole interactome scanning experiment), natural ligands for PRMs are identified by computationally scanning the proteome for sequence patterns similar to known ligands, and these are tested for interactions using synthetic peptide array (SPOT) technology. Proteome scanning can use regular expressions (REs), which describe discrete text patterns, or PWMs, which describe probabilistic positional frequency-based patterns. Although both methods rely heavily on the quality of the information they are based upon, REs run a higher risk of missing candidate ligands , whereas PWMs often fail to catch strict position-specific rules. Following identification of putative natural ligands by either filtering method, the peptides are tested for interactions by SPOT.We used the WISE approach to generate a yeast SH3 domain interaction network independently by creating a set of 15 REs based on SH3 domain specificity profiles identified in this study and previously Peptides identified by either PWMs or REs address the ability of a domain to bind to a ligand outside of its protein and cellular context, but the peptides are identified by independent computational approaches with different strengths and weaknesses. To address this, we used the PWMs to define a set of peptides of similar size to the one defined by the REs. Interestingly, this analysis revealed only an approximately 30% overlap between the peptide sets defined by REs and PWMs. To examine the PWM-defined peptides experimentally, we tested in the SPOT assay the ten peptides with the highest PWM score for each SH3 domain. Of the 230 PWM high-scoring peptides, 113 were not included in the original WISE interactome, and approximately 55 of these gave a significant SPOT signal with at least one SH3 domain . The 55 The SPOT approach is semiquantitative, so we also examined the correlation between interaction signals and dissociation constants, but we found that, as reported previously To complement the phage display and SPOT experiments, we performed large-scale yeast two-hybrid screens. We screened 22 yeast SH3 domain baits against a novel yeast activation domain ORFeome library To assess the potential of identifying biologically relevant interactions, we examined the number of literature-validated interactions that were identified by each approach. To do so, we curated a comprehensive “gold-standard” set of 42 SH3 domain interactions from the literature (see below). Within this gold-standard set, only five were identified by the ORFeome screen, whereas 28 were identified by the gDNA screen. Thus, with yeast SH3 domains, gDNA two-hybrid screening has a 2.5-fold lower false-negative rate than ORFeome analysis , which mx = 1, 2,…N, where N is the size of the yeast proteome) was determined. We find that approximately 10% of two-hybrid positives rank among the top ten hits predicted by the PWM of the associated SH3 domain , since a protein can be targeted by multiple SH3 domains but at different binding sites. Interestingly, the fraction of binding sites targeted by more than one SH3 domain is lower than the fraction of proteins targeted by more than one SH3 domain (29% against 39%), revealing that some proteins have multiple unique binding sites recognized by individual SH3 domains , treating each of them as a single protein since they have highly similar specificity profiles. In this case, we found that 33% of all interactors are targeted by more than one SH3-containing protein in our network . As prevp = 3×10−7 and p = 9×10−8, respectively). Moreover, from a recently published set of approximately 60 known and putative endocytosis proteins, 29 were found to be connected with at least one SH3 domain in our interaction network p-value of 5×10−8As one approach to assessing the biological relevance of interactions identified by the Bayesian model, we examined biological process annotation associated with the putative SH3 domain ligands, defined by Gene Ontology (GO). We found a significant number of overrepresented biological processes known to be associated with yeast SH3 domain biology such as establishment of cell polarity and endocytosis  is recruited, which activates the Arp2/3 complex to promote actin assembly. The SH3 protein Sla1p is thought to inhibit the actin polymerizing function of Las17p. This inhibition appears to be relieved by the recruitment of members of the WASP/Myo complex, including Vrp1p and the SH3 proteins Bbc1p, Myo3p/Myo5p, and Bzz1p. Actin polymerization triggered by the WASP-myosin complex leads to recruitment of the actin module proteins, which include actin, Cap1p, Cap2p, Sac6p, Abp1p (SH3 protein), and the Arp2/3 complex, leading to further actin polymerization. As the vesicle begins its movement into the cell, the scission module, consisting of Rvs161p and the SH3 protein Rvs167p, is recruited. Although the exact scission mechanism is unclear, the scission module promotes the release of the nascent endocytic vesicle Spatiotemporal characterization of protein dynamics by live-cell imaging has provided a detailed view of endocytosis, but our understanding of this pathway is far from complete. It has been established that numerous proteins arrive at sites of endocytosis in a choreographed manner, but it is not known how the sequential recruitment, assembly, and functions of endocytic proteins are achieved. Our Bayesian interaction network contains 29 of the 60 or so known yeast endocytosis proteins, including ten that contain SH3 domains. To gain insights into the roles of SH3-mediated interactions in endocytosis, we screened for putative ligands for these ten SH3 domains using our Bayesian scoring algorithm . The intThe network allows us to map potential interactions onto the temporal order of protein recruitment at the site of endocytosis, and these interactions likely mediate assembly of protein modules and coordinate activities between the modules . We therSubsequent to the formation of an SH3 domain–mediated network within the WASP/Myo module, the network analysis points to the formation of an SH3 domain–mediated network within the actin module , each of which had a high SH3 interaction score with at least one of the established endocytic modules . We predTwo closely related SH3 domain proteins, Lsb3p and its paralog Lsb4p, had high interaction scores across several modules, most significantly with early  and late (actin) modules . NotablyTo test our predictions in vivo, each protein was C-terminally tagged with GFP and expressed from its endogenous locus in yeast cells. The dynamics of each protein were analyzed individually and in tandem with Abp1p-RFP. The dynamic localization analysis validated our approach for assigning proteins to endocytic modules based on their interaction scores .In agreement with earlier observations For Aim21p-GFP, we observed that it is located in immotile patches with a lifetime of 10±1 s, similar to the patch dynamics reported for Bbc1p Scp1p is expected to be part of the actin module as it is predicted to bind the SH3 domain of Abp1p. Indeed, Scp1p-GFP formed patches with a lifetime of 15±2 s  and coloAs mentioned above, Lsb3p and Lsb4p scored highly across all modules, predicting a long lifetime at the patch. As expected, Lsb3p-GFP patches were long-lived with a lifetime of 36±9 s . Lsb3p-GFinally, we analyzed the dynamics of Bsp1p, which our model suggested interacts with proteins in all modules, similar to Lsb3p and Lsb4p. However, in contrast to the Lsb proteins, Bsp1p-GFP patches were short-lived with a lifetime of 13±2 s . Bsp1p-GOur SH3 domain network contains only approximately half of the 60 proteins implicated in endocytosis and, as underscored by the Bsp1p example, a number of SH3-independent interactions must control endocytosis protein localization. To emphasis this point, we also analyzed the dynamics of yeast twinfilin (Twf1p), a highly conserved actin monomer-sequestering protein involved in regulation of the cortical actin cytoskeleton In summary, SH3 domain interactions are powerful predictors of spatiotemporal localization of yeast SH3 domain proteins. The putative SH3 domain–mediated interaction networks allowed us to accurately predict the dynamics of several previously uncharacterized proteins in the endocytic pathway and provided a detailed spatiotemporal map of the endocytic pathway .We generated a specificity map for the SH3 domain family of budding yeast based on 1,871 unique peptide ligands isolated against 25 of the 27 domains. This map reveals that SH3 domains have a high level of intrinsic specificity and different domains recognize distinct sets of ligands. Notably, specificity was observed for ligand positions outside of the core positions, suggesting that SH3 domains utilize multiple features of their peptide ligands to achieve binding selectivity.A major challenge in functional proteomics is the development of accurate protein interaction networks. We have integrated the data from three independent screening techniques  into a Bayesian model to generate a yeast SH3 domain interaction map. Each technique has a semiquantitative measure that was captured by the probabilistic model. Our interaction map captures a significant proportion of literature-validated interactions and therefore serves as an accurate reference for additional in-depth studies of yeast SH3 domain biology. Proper interpretation and use of our interaction map requires consideration of additional factors such as cellular concentration, localization, and competition from other SH3 domains to identify physiologically relevant interactions.Applying our model to proteins involved in endocytosis revealed that there is a significant connection between the time at which a protein arrives at the endocytic patch and its predicted SH3 domain interactions. This correlation was used to accurately predict the spatiodynamics of several uncharacterized endocytic proteins. We found that Scd5p and Aim21p are both components of the WASP/Myo module, which drives vesicle internalization by nucleating actin filament assembly and generating myosin motor-based forces on the actin filaments The SH3 interaction predictions did not agree with the dynamics of Sla1p and Bsp1p. Sla1p appears in the coat module, whereas its predicted interactions are more consistent with it being a component of the WASP/Myo module. This may be explained by its established essential role in regulating the WASP/Myo module Given the conserved nature of endocytosis from yeast to human S. cerevisiae genomic DNA by the polymerase chain reaction (PCR) and cloned into a vector designed for the expression and purification of SH3 domains fused to the C-terminus of glutathione S-transferase, as described For cloning, the SH3 domain boundaries were defined as the union of the domain regions identified by BLAST 10 unique members) fused to the N-terminus of the gene-8 major coat protein of M13 filamentous phage were used to select peptide ligands for the collection of purified GST-SH3 fusion proteins. All domains were first screened using a random decapeptide library . Domains that failed to select peptides with the decapeptide library were subsequently screened using a biased peptide library . Three SH3 domain proteins  were also tested using a biased library containing a fixed positive charge . Phage display selections were carried out as described Phage-displayed peptide libraries (>10SP) for a given column (position) of a PWM was calculated as is done for the letter height in a sequence logo SP value of one means the given PDZ domain is completely specific for a single amino acid at that position, and a value of zero means that there is no preferred amino acid at that position. We have also included a p-value to assess the statistical significance of these scores. The p-values were computed by statistical sampling: for each PWM, we generated 107 sequences of N randomly chosen amino acids, with N equal to the number of different peptides used to build the PWM. For each sequence, we computed the SP score, and from the distribution of SP scores, we computed the p-value of the SP scores for each column in the initial PWM -ether membranes SI) were executed with the software Genespotter (MicroDiscovery) following previously described rules The peptide arrays were incubated with the GST-SH3 fusion proteins, as described S. cerevisiae genomic DNA library, using sequence specific primers fused to common sequences used for homologous recombination cloning. Specifically, the forward primer was composed of the bait-specific primer and a 23-nucleotide common sequence (CGACCCCGGGAATTCAGATCTAC), which is homologous to the upstream sequence of the SpeI site on pPC97 CGGGGACAAGGCAAGCTAAACTA), which is homologous to the 5′ of the KanMX6 cassette. The KanMX6 cassette was amplified by PCR with the forward primer (TTTAGCTTGCCTTGTCCC) and the reverse primer (ATAGATCTCTGCAGGTCGACGGATCCCCGGGAATTGCCATTTTTCGACACTGGATGGC), using a KanMX6 cassette carrying plasmid, p2076, as template. Along with the PCR-amplified KanMX6 cassette and the SpeI-cut pPC97, bait coding sequence PCR product was transformed into Y8930 , which was generated from PJ69-4α Escherichia coli DH5-α. Constructed plasmid was purified from kanamycin-positive clone, verified by DNA sequencing, and transformed into Y8930 for Y2H screening.DNA fragments encoding SH3 domains were amplified by PCR from a The whole library was assembled in an 1,536-spot array format on agar plates with each clone represented twice. ORFeome Y2H screening was performed as described All pOBD plasmids were taken from Tong et al. http://www.ncbi.nlm.nih.gov/sites/entrez) and the Saccharomyces Genome Database  To compile a comprehensive list of yeast SH3 domain–ligand interactions supported by one or more experiments (referred to as the gold-standard set), we used a combination of automatic text mining and database searches to retrieve abstracts from the literature. The DOMINO database, specialized in domain–peptide interactions, already contained 22 entries for yeast SH3 domains, curated according to the MIMix standards from 14 papers The peptides from phage display were converted into position weight matrices (PWMs) by calculating the probability of occurrence for each amino acid at each position. Despite the large number of peptide sequences, we still substantially undersampled sequence space, and hence added pseudocounts. We scaled the number of pseudocounts added by the entropy of each position We employed the Bayesian Network algorithm as implemented in the WEKA 3.4.13 Java libraries To ensure a reliable set of gold-standard true-positive interactions for efficient machine learning, we used the curated list of 42 bona fide domain–peptide interactions for the yeast SH3 domains deposited in the DOMINO database, as described above Kd = 1.5 µM.Performance of each data source was evaluated using the AUC (area under the curve) in the ROC curve. This corresponds to an evaluation of how well each data source corresponds to the gold-standard data. Finally, the performance of the Bayesian data integration was assessed using the AUC in a ROC curve analysis with 10-fold cross-validation. Ten-fold cross-validation corresponds to splitting the gold standard into a training (9/10) and a testing (1/10) set ten times in a rotating fashion and evaluating its accuracy for each split. Using the gold-standard set, we classified the discretized input data into the “True” (interacting) and “False” (noninteracting) labels as well as a probability score of the interaction. We report all interactions assigned a probability score of >0.6 . The netYeast strains were grown at 25°C in standard rich medium (YPD) or synthetic medium (SD) supplemented with appropriate amino acids. GFP tags were integrated chromosomally to generate C-terminal fusions of each protein, as described For microscopy, cells were grown in SD medium without tryptophan (to minimize autofluorescence) at 25°C until early log phase. Cells were attached to coverslips coated with concanavalin A, which were sealed to slides with vacuum grease (Dow Corning). Imaging was done at room temperature using an Olympus IX81 or IX71 microscope equipped with 100× NA 1.4 objectives, and Orca II cameras (Hamamatsu). Simultaneous two-color imaging was done using an image splitter  to separate the red and green emission signals to two sides of the camera sensor using a 565-nm dichroic mirror, and 530/30-nm and 630/50-nm emission filters. To excite GFP or RFP, we used a 488-nm Argon ion laser (Melles Griot) or a mercury lamp filtered through a 575/20-nm filter, respectively. The excitation beams from these two light sources were combined using a beam splitter. After each experiment, images of immobilized microbeads that fluoresce at both green and red wavelengths were captured. These images were used to align the cell images.http://rsbweb.nih.gov/ij/) was used for general manipulation of images and movies.Image analysis was done as described Figure S1Yeast SH3 domain specificity map with distances. PWMs were generated using phage-derived binding peptides, and a PWM-based scoring algorithm was used to search the yeast proteome for closely matching sequences, which were subsequently aligned in an unrooted clustergram. The specificity profile for each SH3 domain is represented next to the name. The SH3 domain specificity classes are colored as follows: I (red), II (blue), and III (green). Specificity profiles that could not be assigned to any class are shown in black. Underlined names indicate domains that exhibit two distinct specificity profiles. The distances on the map represent Euclidean distances based on the feature vector from the endogenous peptides.(0.92 MB EPS)Click here for additional data file.Figure S2Phage-derived specificity profiles correlate with binding affinities. In vitro affinity data for binding to the SH3 domain of Abp1p were used to calculate the differences in Gibbs free energy (ΔΔG) for various peptides targeted relative to a reference peptide , and these were plotted against the score match to the phage-derived PWM (x-axis).(0.30 MB EPS)Click here for additional data file.Figure S3Gold-standard interactions identified by yeast two-hybrid. The number of yeast two-hybrid interactions identified in the ORFeome (blue line) and gDNA (red) screening approaches were examined for interactions recapitulated in the gold-standard set (green).(1.51 MB EPS)Click here for additional data file.Figure S4Performance analysis following redundancy reduction. ROCs were plotted against a gold-standard set of interactions following exclusion of all domains with highly similar binding profiles. The Bayesian model was retrained and its accuracy assessed by 10-fold cross-validation.(0.25 MB EPS)Click here for additional data file.Figure S5Bayesian model is unbiased toward the experimental technique used in the gold-standard interactions. ROCs were plotted following bootstrapping of the gold standard positive set to exclude interactions that were only found by one specific technique. The plot denoted by “All” represents the ROC curve for the Bayesian model incorporating all interactions from the gold-standard set, whereas each of the other plots is based on the exclusion of interaction data from the gold-standard set based on a given experimental technique: mutagenesis (Mutag), coimmunoprecipitation (CoIP), overlay assay (Overlay), fluorescence titration (Fluor), pull-down (Pulld), alanine substitution analysis (Subst), and yeast two-hybrid (Twohy). p-values are calculated based on 10-fold cross-validation.(0.32 MB EPS)Click here for additional data file.Figure S6Yeast SH3 domain interaction network. Network representation of SH3 domain mediated interactions predicted by the Bayesian model. SH3 domains are represented as diamonds with their associated interactors shown in magenta circles. Although some SH3 domains recognize more than one class of peptides, each SH3 domain is represented as a single node, therefore putative binding ligands conforming to different specificity classes are shown binding to a unique domain. Domains with putative roles in endocytosis as described in Kaksonen et al. (0.96 MB EPS)Click here for additional data file.Figure S7Some proteins are targeted by multiple SH3 domains. For each SH3 domain, the fraction of interactors targeted by other SH3 domains was calculated at the level of the full-length protein (black bars) and by considering each binding site individually (grey bars).(0.97 MB EPS)Click here for additional data file.Figure S8Paralogous SH3 domains account for some cross-reactivity in the interaction network. For each SH3-containing protein, the fraction of interactors targeted by other SH3 domains was calculated at the level of the full-length protein (black bars) and by considering each binding site individually (grey bars), following merging of four pairs of closely related SH3 domains from Boi1p/Boi2p, Lsb1p/Pin3p, Lsb3p/Lsb4p, and Myo3p/Myo5p.(0.37 MB EPS)Click here for additional data file.Figure S9Six-core subgraph network. An interconnected core of 31 proteins, where each protein has at least six interactions (k-core = 6) is shown. SH3 domains are represented in diamonds with their associated interactors shown in magenta circles. Although some SH3 domains recognize more than one class of peptides, each SH3 domain is represented as a single node; therefore, putative binding ligands conforming to different specificity classes are shown binding to a unique domain. Domains with putative roles in endocytosis as described in Kaksonen et al. (0.43 MB EPS)Click here for additional data file.Table S1Yeast SH3 domains used in phage-display analysis. SH3 domains are named according to the gene name in which they were identified. SH3 domains from proteins with more than one domain are numbered from the N-terminus and demarcated from the protein name with a dash. The listed amino acid ranges indicate the length of the constructs used in this analysis and not necessarily the SH3 domain boundaries defined by computational analysis. For each domain, we list whether a stable GST fusion protein was isolated. All domains were initially screened with a random decamer peptide library . Domains that failed to select peptides with the X10 library, were screened with a biased library . The Cyk3p and Lsb4p domains were also screened with a biased library containing a fixed charged amino acid . For each construct, we list whether a stable GST fusion protein was isolated and if the latter selected peptides in the phage display analysis. Domains retested based on a fungal species sequence alignment are denoted by an asterisk (*).(0.02 MB PDF)Click here for additional data file.Table S2Summary of analyzed SH3 domains with boundaries identified from fungal species alignments. The domain boundaries for three SH3 domains were extended based on fungal species alignments. SH3 domains are named according to the gene name in which they were identified. SH3 domains from proteins with more than one domain are numbered from the N-terminus and demarcated from the protein name with a dash. The listed amino acid ranges indicate the length of the constructs used in this analysis and not necessarily the SH3 domain boundaries defined by computational analysis. Sla1-1/2 indicates the construct encoding the first two N-terminal SH3 domains from Sla1p in tandem. For each domain, we list whether a stable GST fusion protein was isolated. All domains were initially screened with a random decamer peptide library . Domains that failed to select peptides with the X10 library, were screened with a biased library . The Sla1-1/2 construct was also screened with a biased library containing a fixed-charged amino acid . The library or libraries used to select peptides for each domain are indicated. For each construct, we list whether a stable GST fusion protein was isolated and if the latter selected peptides in the phage display analysis.(0.02 MB PDF)Click here for additional data file.Table S3Position-specific SP scores.SP scores are shown for every position in the PWM for each SH3 domain. A p-value based on randomized peptides Click here for additional data file.Table S4Sho1p-SH3 peptide-ligand affinities with associated PWM scores and ΔΔG values. Sho1p-SH3 ligand affinities were taken from Zarrinpar et al. G values were taken as −RT ln (Kd Peptide 1/Kd Peptide 2) and were calculated relative to a reference peptide (IRSKPLPPLPV).(0.03 MB PDF)Click here for additional data file.Table S5Abp1p-SH3 peptide-ligand affinities and associated PWM scores and ΔΔG values. Abp1p-SH3 ligand affinities were taken from Stollar et al. G values were taken as −RT ln (Kd Peptide 1/Kd Peptide 2) and were calculated relative to a reference peptide (KKTKPPVPPKPSHLKPK).(0.03 MB PDF)Click here for additional data file.Table S6Predicted yeast SH3 domain ligands based on regular expressions. A set of 15 regular expression patterns were used to scan the yeast proteome for predicted SH3 domain ligands. This analysis identified 2,953 peptides within 1,693 proteins. The regular expression pattern, matching ligand sequence, location within the ORF (Start and End), including the common gene name are shown for each predicted ligand.(0.35 MB PDF)Click here for additional data file.Table S7SPOT intensities for yeast SH3 domain ligands predicted by regular expressions. The peptides predicted by the set of regular expressions (ressions  were tes(1.09 MB PDF)Click here for additional data file.Table S8SPOT intensities for the top ten highest scoring yeast SH3 domain ligands based on PWM matches. SPOT intensities were measured for the ten best-predicted matches for each SH3 domain specificity profile based on a PWM scoring algorithm. For domains that recognized more than one set of ligands, the ten best-predicted ligands from each PWM is shown. The peptides were arrayed in the order indicated, where the 11th peptide represents a synthetic, phage-optimized peptide. The peptides were arrayed in duplicate, and the signal intensity from each SPOT experiment is shown. For domains that recognized two sets of ligands, the first row of the array represents peptide ligands predicted by the first PWM, whereas the second row contains peptide ligands predicted by the second PWM. In these cases, rows 3 and 4 of the array are duplicates of rows 1 and 2, respectively. The specificity profile used to generate each PWM shown below the blot from the SPOT experiment. Sla1-1/2-W41S and Sla1-1/2-W108S represent the two point mutations made in the Sla1-1/2 construct to determine peptide ligand interactions for each SH3 domain individually.(0.62 MB PDF)Click here for additional data file.Table S9Summary of yeast SH3 domains analyzed by yeast two-hybrid. Yeast SH3 domains were cloned into the pPC97 and pBDC/pOBD yeast two-hybrid expression vectors for use in the ORFeome and fragmented gDNA screening approaches, respectively. SH3 domains are named according to the gene name in which they were identified. SH3 domains from proteins with more than one domain are numbered from the N-terminus and demarcated from the protein name with a dash. The listed amino acid ranges indicate the length of the constructs used in this analysis and not necessarily the SH3 domain boundaries defined by computational analysis. Sla1-1/2-W41S and Sla1-1/2-W108S represent the two point mutations made in the Sla1-1/2 construct to determine the binding partners for each SH3 domain individually. Each domain was tested in both screening techniques. In most cases, the addition of a competitor of HIS3 gene product, 3-amino-1,2,4-triazole (3-AT) was added to reduce the level of basal transcription as indicated. The number of isolated hits is indicated for each SH3 domain. The SH3 domains that could not be cloned into either expression vector are also indicated.(0.03 MB PDF)Click here for additional data file.Table S10Yeast SH3 domain interactors isolated by yeast two-hybrid. Interacting ORFs isolated by ORFeome or gDNA screening are shown for each SH3 domain, including the number of times a given interacting ORF was captured. The ORF and gene name encoded in each activation domain (AD) isolated from a positive yeast two-hybrid colony are indicated. SH3 domains are named according to the gene name in which they were identified. SH3 domains from proteins with more than one domain are numbered from the N-terminus and demarcated from the protein name with a dash. Sla1-1/2-W41S and Sla1-1/2-W108S represent the two point mutations made in the Sla1-1/2 construct to determine the binding partners for each SH3 domain individually.(0.11 MB PDF)Click here for additional data file.Table S11Gold-standard yeast SH3 domain interactions. The literature was manually curated for yeast SH3 domain-mediated interactions. The PubMed Identifier (PMID) is shown for the paper in which the interaction was identified. Each row represents a unique SH3 domain-ligand interaction with their associated UniProt identification numbers (SPID). The amino acid (A.A.) range and sequence for the interacting protein (or protein fragment) is shown along with the techniques used to identify it . The biological relevance of the SH3-mediated interaction as described in the paper is also reported. The list contains redundant interactions as some SH3 domain mediated interactions were reported in more than one paper. A brief description of the experimental techniques listed is provided as follows: 1) Overlay assay: the SH3 domain protein is run on SDS gel and transferred to nitrocellulose. The membrane is then probed with the interactor (or vice versa); 2) Spot synthesis overlay: peptides are chemically synthesized on array format, and the membrane is probed with GST-SH3 fusion protein; 3) Phage-display/two-hybrid: random nonapeptides are selected with phage display and compared with yeast two-hybrid analysis to map the peptide; 4) Alanine scanning: each amino acid of the binding peptide is substituted with Ala and the effect of the mutation on SH3 binding is analyzed; 5) Mutagenesis analysis: each amino acid of the binding peptide is mutated with any other amino acid and the effects of the mutation on SH3 binding are analyzed; 6) Co-IP: the SH3 domain is immunoprecipitated and the presence of interactors in the complex is revealed by western blot analysis with specific antibodies; and 7) Affinity coprecipitation: the SH3 domain is precipitated with an affinity column (GST column) and the presence of interactors in the complex is revealed in Western blot analysis with specific antibodies.(0.09 MB PDF)Click here for additional data file.Table S12Predicted yeast SH3 domain interactions based on Bayesian networks. The yeast proteome was scanned for putative SH3 domain interactions based on our Bayesian model. The Bayesian model assigns a probability score based on how well a given interaction scored across all three independent techniques, where higher scores represent higher confidence predictions. To obtain a wider spectrum of scores, the probability scores were scaled by taking −log(1 − Probability score). The best matching peptide based on a PWM scoring algorithm from each predicted gene is shown.(0.11 MB PDF)Click here for additional data file.Table S13Protein dynamics and interaction scores for endocytosis proteins in yeast. The modular localization is represented for all proteins with characterized dynamics during endocytosis. The proteins are separated into their known endocytic modules. For each protein, the total predicted SH3-mediated interaction score  for every endocytic module  is calculated. The time frame represents the point at which the protein appears (START) and is no longer observed (END) during endocytosis, with the lifetime taken as the difference between the two time points. Each protein was predicted to be part of the module for which it obtained the highest interaction score (highlighted in yellow). Cross-module proteins were considered to be proteins with interaction scores for a particular module that did not exceed the median interaction score across all modules by more than 2-fold. We could not predict the localization of Rvs167p because its corresponding scission module is only comprised of two proteins, itself and Rvs161p, which is not predicted to bind to any SH3 domain proteins required during endocytosis. To reduce the effect of weak interactions, predictions were only made for proteins that had module interactions scores of five and higher. Based on this criterion, the modular localization of some proteins could not be predicted (N/A).(0.03 MB PDF)Click here for additional data file.Text S1Supporting Materials and Methods.(0.05 MB DOC)Click here for additional data file.


Plasmodium falciparum chimeric protein PfCP-2.9 is a promising asexual-stage malaria vaccine evaluated in clinical trials. This chimeric protein consists of two cysteine-rich domains: domain III of the apical membrane antigen 1 (AMA-1 [III]) and the C-terminal region of the merozoite surface protein 1 (MSP1-19). It has been reported that the fusion of these two antigens enhanced their immunogenicity and antibody-mediated inhibition of parasite growth in vitro.The 15N-labeled and 13C/15N-labeled PfCP-2.9 was produced in Pichia pastoris for nuclear magnetic resonance (NMR) structure analysis. The chemical shift assignments of PfCP-2.9 were compared with those previously reported for the individual domains  or PfMSP 1-19). The two-dimensional spectra and transverse relaxation rates (R2) of the PfMSP1-19 alone were compared with that of the PfCP-2.9.The R2) strongly suggests no weak interaction between the domains.Confident backbone assignments were obtained for 122 out of 241 residues of PfCP-2.9. The assigned residues in PfCP-2.9 were very similar to those previously reported for the individual domains. The conformation of the PfMSP1-19 in different constructs is essentially the same. Comparison of transverse relaxation rates (These data indicate that the fusion of AMA-1(III) and MSP1-19 as chimeric protein did not change their structures, supporting the use of the chimeric protein as a potential malaria vaccine. Plasmodium falciparum are attractive candidates for malaria vaccines ) and grown to an optical density of approximately 20 at 600 nm (OD600). The cells were then transferred into 3L of 15N salt base medium (2.67% [v/v] H3PO4 (85%), 0.0894% CaSO4, 1.52% K2SO4, 1.49% MgSO4· 7H2O, 0.413% KOH, 4% glycerol, 0.4% [v/v] PTM1 salts, 0.9% [NH4]2SO4) in a 5-L fermenter. OD600 reached 75 after 21 hr, and 180 g methanol was then added to induce expression of the chimeric protein. After 19 hr, the culture was centrifuged at 6000 × g for 20 min at 4°C, and the supernatant was collected for protein purification.The stock s strain  expressi2PO4, 300 mM NaCl, pH 8.0) without a reducing agent. The cell culture supernatant was then applied to the column at 1 ml/min, and flow through was collected for analysis. The column was washed with 60 ml loading buffer followed by 120 ml washing buffer . Bound proteins were eluted with 250 mM imidazole. Eluted proteins were concentrated into 1 ml using an Amicon Ultra-15 centrifugal filter unit .The target protein was purified by Ni-NTA agarose column  affinity purification. Ten milliliters of Ni-NTA agarose was equilibrated with the loading buffer (50 mM NaH2O/10% D2O (pH 7.4). The sample was argon-flushed.The NMR sample was prepared in a buffer containing 20 mM sodium phosphate in 90% HP. pastoris strain was streaked on a YPD agar plate and grown in 3 L of 15N salt base as described above. The cell density reached OD600 of 60 after 17 hr, and 5 g of 13C-D-glucose was then added. After 2 hr, 50 g of 13C-methanol was added to induce chimeric protein expression. After 6 hr, the culture was centrifuged at 6,000 × g for 20 min at 4°C, and the supernatant was collected for protein purification.The stock 15N-labeled PfCP-2.9. The final volume was 1.2 ml. Protein concentration was 10 mg/ml as determined by the Bradford method, and purity was > 90% as assessed by SDS-PAGE.The purification and NMR sample preparation of the target protein was same as for P. pastoris strain was streaked on a YPD agar plate containing 0.25 mg/ml G418, which was preserved in laboratory. A clone was incubated in 500 ml BAGY medium 2SO4, 4 × 10-5% biotin, and 100 mM potassium phosphate [pH 6.0]) and grown to OD600 of approximately 10. Cells were collected by centrifugation and transferred into 500 ml of 15N BMM medium 2SO4, 4 × 10-5% biotin, and 100 mM potassium phosphate [pH 6.0]). Every 12 hr, 1.25 ml of methanol was added to the culture medium. After 72 hr, the culture was centrifuged at 6,000 × g for 20 min at 4°C to collect the supernatant for protein purification. The purification process and NMR sample preparation was the same as for PfCP-2.9.The stock 15N-TOCSY-HSQC experiments were performed for side-chain assignments [15N-edited NOESY-HSQC spectrum  was performed to confirm the chemical shift assignments. All spectra were processed using the software package NMRPipe [The nuclear magnetic resonance (NMR) spectra were acquired at 25°C with a Bruker Avance 500 (with a CryoProbe) and 800 MHz spectrometers, equipped with four RF channels and a triple-resonance probe with pulsed field gradients. The three-dimensional (3D) HNCA, HNCACB, CBCA(CO)NH, HNCO, and HN(CA)CO experiments were carried out for the backbone assignments. HBHA(CO)NH, (H)CC(CO) NH, CC(CO)NH, and ignments . 3D 15N- NMRPipe .15N transverse relaxation experiments of the PfCP-2.9 and MSP 1-19 domain were recorded [1H) and 70 (15N) complex data points were collected with 48 transients per increment and a recycle delay of 3.0 s. The delays were 4 (×2), 32, 60, 100, 160 ms for 15N-labeled MSP 1-19, and 4 (×2), 12, 28, 48, 72, 100, 160 ms for 15N-labeled PfCP-2.9. The transverse relaxation rate constants (R2) were obtained by fitting the peak intensities to a single exponential function by nonlinear least-squares method using MATLAB [The backbone recorded  on a Bru15N-edited heteronuclear single quantum coherence (HSQC) spectrum of PfCP-2.9  were obtained for residues Ser15, Lys56, Phe75, Thr88, Glu102, and Pro111. The mapping of these assigned residues onto the NMR structure of the PfAMA-1(III) domain (PDB code 1HN6) is shown in Figure The chemical shift assignments of PfCP-2.9 were performed based on the triple-resonance NMR spectra aided by the previously reported NMR assignments for the PfAMA-1(III) domain  and PfMSP1-19 domain (BMRB entry 4437) ,30. Backα and/or Cβ atoms are His150-Cys152, Lys154, Asn160-Arg165, Asp168-Glu171, Leu176-Asn189, Gln191, Thr193-Glu196, Asn198-Lys225, Asp227-Tyr229, and Phe232-Ser238. Additional chemical shift assignments of the Cα and Cβ atoms (and in most cases Hα and Hβ atoms) can be obtained for residues Gln149, Val153, Gln159, Leu167, Pro190, Pro192, Asn197, Pro226, and Leu231. The mapping of these assigned residues onto the NMR structure of the MSP 1-19 domain (PDB code 1CEJ) is displayed in Figure For the PfMSP1-19 domain, the residues with assigned backbone amides as well as CN| < 0.2 ppm, |ΔN| < 1.5 ppm, |ΔCα| < 1.0 ppm, |ΔCβ| < 1.5 ppm, and |ΔCO| < 1.0 ppm. Only a few atoms show relatively large chemical shift differences between the present results and previously published assignments of AMA1 III (BMRB entry 4787) [N atoms of Lys43 and Lys79, the backbone 15N atom of Lys43, the Cα atom of Asp105, and the Cβ atoms of Cys63 and Asp105. The difference might be due to the fact that in the NMR study of AMA1 III domain alone was performed using refolded protein under acidic (pH 3.4) buffer condition [As shown in Figure ry 4787) . These iondition , while iN| < 0.1 ppm, |ΔN| < 1.0 ppm, |ΔCα| < 0.5 ppm, |ΔCβ| < 1.0 ppm, |ΔCO| < 0.5 ppm. Few atoms show bigger chemical shift differences between the current results and previously published assignments of PfMSP1-19 (BMRB entry 4437) [15N atoms of Glu169, Glu171 and Ala203, and the Cα atom of Ser238. The Glu169 and Glu171 residues are acidic residues, whereas the Ala203 residues are located between two Asp residues. The larger 15N chemical shift differences for these three residues probably result from the pH differences of buffers used in the two studies (buffer pH used in the previous study of the PfMSP1-19 domain was 6.5).For the residues assigned in the PfMSP1-19 domain, the majority show only small chemical shift differences: |ΔHry 4437) ; these iP. pastoris and dissolved in the same buffer as PfCP-2.9. Unfortunately, the 15N-edited HSQC spectrum of the 15N-labeled PfAMA-1(III) domain indicated that the separately expressed protein domain was not properly folded; however, the 15N-edited HSQC spectrum of 15N-labeled PfMSP1-19 domain showed well-dispersed peaks, demonstrating the protein had folded well. The overlay of the HSQC spectra of PfMSP1-19 and PfCP-2.9  and PfMSP1-19 domains were expressed separately in 9 Figure  were witThe lack of change in chemical shifts of the PfMSP1-19 domain when expressed as a part of the fusion protein PfCP-2.9 excludes the possibility of strong interactions between PfMSP1-19 and PfAMA-1(III) domains; however, weak interactions between the two domains are possible. If weak interactions between the two domains exist, one or both of the following phenomena were expected to seen: (1) chemical shift differences for residues at the interaction surface; (2) line broadening due to chemical/conformational changes.R2 of the two samples were measured to further investigate the possibility of line broadenings. For the data analysis of PfCP-2.9, only those residues present in PfMSP1-19 were analysed. The peaks that overlapped were excluded from the analysis. Further, residues His150, Glu171, Asp184, Gly213, Ser228, and Ser238 showed weak signals and poor relaxation curves in both samples, thus were also removed from the final analysis. The R2 values of a total of 36 residues  were finally obtained.Neither chemical shift changes between the two spectra nor peak disappearance was observed; therefore, the transverse relaxation rates R2 values of these residues were compared between PfCP-2.9 and PfMSP1-19. The R2 values were overall higher for residues in PfCP-2.9 compared with those in PfMSP1-19, which is due to the higher molecular weight and slower molecular tumbling of PfCP-2.9. By overlaying the two datasets  domain or the linker region.The s Figure , the treIt has been reported that a panel of monoclonal antibodies recognizing conformational epitopes on PfMSP1-19 interacted with the chimeric protein with reduced sensitivity . Of thesIn the present study, the chemical shift assignments of PfCP-2.9 was compared with those previously reported for individual domains and found that the structures of the two domains were unchanged. Further, comparison of the 2D spectra of PfMSP1-19 with the domain in the PfCP-2.9 also demonstrated that the conformation of the domain is essentially the same, while the PfAMA-1(III) domain showed better folding in the chimeric protein than it did alone.R2 between the PfMSP1-19 domain in PfCP-2.9 and the domain alone strongly suggests no weak interactions between domains. Taken together, these data suggested that the structures of the PfAMA-1(III) and PfMSP1-19 domains were not altered by fusion into the chimeric protein.Comparison of transverse relaxation rates 15N-labeled and 13C/15N-labeled PfCP-2.9 were expressed in P. pastoris for NMR structure analysis. The backbone chemical shift assignments can be obtained for a reasonable portion of the residues in PfCP-2.9. By comparing with the previously reported studies of the two domains alone, high similarity of chemical shifts was observed. Relatively larger differences were observed for only a small number of residues, which may be due to different experimental conditions . In fact, almost all the residues that showed significant chemical shift changes in PfAMA-1(III) were charged residues, with the exception of Cys63, which is near charged residues according to the NMR structure of PfAMA-1 (III). In addition, PfCP-2.9 was modified in the following ways: three glycosylation sites were removed by changing Asn to Gln, a hinge consisting of 28 residues was inserted between the two domains [Crystallization the chimeric protein were unsuccessful despite various efforts. Therefore,  domains , and a 6in vitro, providing additional evidence for the unchanged protein conformation. Moreover, both components of the chimeric protein are able to generate inhibitory antibodies against parasite growth, indicating that both domains fold correctly [The PfCP-2.9 chimeric protein induces antibodies that inhibit parasite growth orrectly . NMR finThe authors declare that they have no competing interests.15N-labled and 13C/15N-labled PfCP-2.9. CJ participated in study design, NMR data analysis, and contributed to manuscript writing. WP conceived of the study, participated in its design, data analysis, and contributed to manuscript writing and editing. All authors read and approved the final manuscript.HP participated in the study design, performed the experiments and data analysis and wrote the manuscript. YH performed the NMR experiment, analysed the NMR data and contributed to manuscript writing. AZ prepared the


Eukaryotic translation elongation factor eEF1A directs the correct aminoacyl-tRNA to ribosomal A-site. In addition, eEF1A is involved in carcinogenesis and apoptosis and can interact with large number of non-translational ligands.There are two isoforms of eEF1A, which are 98% similar. Despite the strong similarity, the isoforms differ in some properties. Importantly, the appearance of eEF1A2 in tissues in which the variant is not normally expressed can be coupled to cancer development.We reasoned that the background for the functional difference of eEF1A1 and eEF1A2 might lie in changes of dynamics of the isoforms.It has been determined by multiple MD simulation that eEF1A1 shows increased reciprocal flexibility of structural domains I and II and less average distance between the domains, while increased non-correlated diffusive atom motions within protein domains characterize eEF1A2. The divergence in the dynamic properties of eEF1A1 and eEF1A2 is caused by interactions of amino acid residues that differ between the two variants with neighboring residues and water environment.The main correlated motion of both protein isoforms is the change in proximity of domains I and II which can lead to disappearance of the gap between the domains and transition of the protein into a "closed" conformation. Such a transition is reversible and the protein can adopt an "open" conformation again. This finding is in line with our earlier experimental observation that the transition between "open" and "closed" conformations of eEF1A could be essential for binding of tRNA and/or other biological ligands.The putative calmodulin-binding region Asn311-Gly327 is less flexible in eEF1A1 implying its increased affinity for calmodulin. The ability of eEF1A1 rather than eEF1A2 to interact with Ca2+/calmodulin is shown experimentally in an ELISA-based test.We have found that reversible transitions between "open" and "close" conformations of eEF1A provide a molecular background for the earlier observation that the eEF1A molecule is able to change the shape upon interaction with tRNA. The ability of eEF1A1 rather than eEF1A2 to interact with calmodulin is predicted by MD analysis and showed experimentally. The differential ability of the eEF1A isoforms to interact with signaling molecules discovered in this study could be associated with cancer-related properties of eEF1A2. Higher eukaryotic translation elongation factor eEF1A operates in translation cycles by directing the correct aminoacyl-tRNA to the A site of mRNA-programmed ribosome . BesidesHigher eukaryotic eEF1A has so far been resistant to attempts to crystallize it, so X-ray analysis is available only for the complex of yeast eEF1A with the truncated GDP exchange subunit eEF1Ba ,12. The Complete conformational sampling for a three-domain protein may require an MD trajectory of a relatively long time scale. However, the protein can adopt denatured forms during simulation, and if the only trajectory is available, the denatured state can be interpreted as the native conformation in solution. Moreover, statistical errors accumulate at long MD calculation. In view of the aforesaid, an alternative, multiple MD simulation method ,14 has bOur multiple MD simulation studies demonstrated more inter-domain mobility of the eEF1A1 molecule when compared to eEF1A2. At the same time eEF1A2 was characterized by a higher internal mobility of the structural domains. Amino acid residues were determined, for which flexibility is evidently different in the isoforms. Some significant MD characteristics inherent to both isoforms were also revealed; in particular, fluctuation of the eEF1A molecule between "open" and "closed" conformations in solution was shown providing for the first time a description of the dynamic behavior of human eEF1A in solution. Importantly, a difference in putative calmodulin binding sites of the isoforms has been predicted. The data obtained are an essential step in the move towards an understanding of the functional divergence of the near-identical eEF1A1 and eEF1A2 isoforms, and in particular, the cancer-related properties of the latter.β-strands , which form the β-sheet surrounded by 8 α-helices . The second domain is the β-barrel formed by the strands Leu248-Leu250, Asp252-Ile256, Gly260-Val267, Met276-Ala281, Val/Ile285-Val289, Ser291-Met294, Glu297-Leu299, Asp306-Val312 and Asn324-Ser329. The third domain is also in the β-barrel consisting of β-strands Gly/Gln339-Leu347, Tyr357-Cys363, Ala366-Asp380, Lys385-Gly/Asn390, Asp398-Gly407, Gly422-Asp428 and Gln431-Asp/Glu442. In the present study the residues up to Pro238 are attributed to domain I, because Pro238 is the last residue from the continuous series of residues in the unstructured chain Cys234-Arg247, which are situated within the distance of the van der Waals radii sum to a residue from the other chain of the domain I (Asp110 for Pro238). Similarly, Pro241 is the first residue from the chain Cys234-Arg247, which is within the distance of the van der Waals radii sum from a residue of other chain of the domain II (Gly270), so Pro241 is chosen as the first residue of the domain II. Ser329 is the end of the domain II because it is the last residue in the Asn324-Ser329 β-strand. Domain III starts with Met/Gln335, which is situated within the distance of the van der Waals radii sum from a residue of other chain of the domain III (Cys411). Domains I and III and also domains II and III are situated tightly one to another, while a voluminous space exists between domains I and II. The space is limited by loop Arg69-Leu77 from the side of domain I and by residues His295-Gly305 from the side of domain II.The models consist of three domains Figure . The firσ value than rmsd of eEF1A2, the eEF1A1 molecule should have more conformational space than eEF1A2.Table σ) for eEF1A2 than for eEF1A1. So, the internal motions of the domains are larger in the eEF1A2 molecule. The difference in mobility of the two protein variants is mostly observed for domain III  and archaebacteria Sulfolobus solfataricus [PDB: 1JNY] as templates. The identity of yeast eEF1A and human eEF1A1 and eEF1A2 variants is 80.7 and 79.6% respectively  at the eEF1A1 and eEF1A2 surfaces. 39463 and 41270 SPC (Single Point Charge) GDP exchange test as in [The eEF1A1 and eEF1A2 isoforms were isolated from rabbit liver and muscle respectively as described by us earlier . The actst as in . Calmodust as in .2+-calmodulin was measured by an indirect Enzyme-linked Immunosorbent Assay using the procedure described in [μg) in 100 mkl of TS buffer  was coated overnight at 4°C in a 96-well polystyrene microtiter plate (Dynatech microtiter). The wells were rinsed five times with 200 μl of TS buffer. After blocking with 200 μl of blocking buffer  for 1 h at room temperature and washing five times with 200 μl of TS, 100 μl of 2000-fold-diluted in blocking buffer mouse monoclonal anti-eEF1A antibody and varying amounts of Ca2+-calmodulin  were added to wells and incubated at room temperature for 2 h. Ca2+-calmodulin was prepared by addition of CaCl2 to calmodulin  After washing five times with 200 μl of TS buffer, 100 μl of 5000-fold-diluted secondary goat anti-mouse IgG antibody conjugated to horseradish peroxidase were added per well and incubated for 1 h at room temperature. Then unbound antibodies were removed by five washes of 200 μl of TS buffer. After addition of 100 μl of ABTS  at 0.5 mg/ml, the incubation proceeded at room temperature for 10–15 min. The absorbance was estimated at 405 nm in a Tecan Sunrise ELISA plate reader. The experiments were performed several times to confirm reproducibility.In vitro binding of eEF1A (eEF1A1 and eEF1A2) with Caribed in . PurifieDSK carried out MD simulation, MD analysis and drafted the manuscript; OVN participated in MD data analysis and carried out the biochemical experiments; CMA participated in design of the study and helped to draft the manuscript, BSN developed the concept of the study and participated in its coordination, AVE participated in data analysis and interpretation and helped to draft the manuscript. All authors read and approved the final manuscript.Spatial model of human eEF1A1 with posttranslational modifications, in PDB format.Click here for fileSpatial model of human eEF1A2 with posttranslational modifications, in PDB format.Click here for fileα-atoms trace root-mean-square deviation from the initial protein conformation. a, b – full protein, c, d – domain I, e, f – domain II, g, h – domain III. a, c, e, g – eEF1A1: red – trajectory 1, green – trajectory 2, blue – trajectory 3, cyan – trajectory 4, magenta – trajectory 5, yellow – trajectory 6. b, d, f, h – eEF1A2: red – trajectory 7, green – trajectory 8, blue – trajectory 9, cyan – trajectory 10, magenta – trajectory 11, yellow – trajectory 12, orange – trajectory 13. Black – average curves.The CClick here for fileMovie of eEF1A1 molecular dynamics, trajectory 6, cartoon representation. "Closed" conformation is formed.Click here for fileMovie of eEF1A2 molecular dynamics, trajectory 13, cartoon representation. "Closed" conformation is formed.Click here for fileMovie of eEF1A1 molecular dynamics, trajectory 2, cartoon representation. "Open" conformation is maintained.Click here for fileMovie of eEF1A2 molecular dynamics, trajectory 9, cartoon representation. "Open" conformation is maintained.Click here for fileFile in .txt format. Content of the file should be inserted to the end of ffg43a2.rtp file of GROMOS96 43a2 force field for supporting non-typical amino acid residues: N-methyllysine (MLZ), protonated N-methyllysine (MLZH), N-dimethyllysine (MLY), protonated N-dimethyllysine (MLYH), N-trimethyllysine (M3L) and L-glutamyl 5-glycerylphosphorylethanolamine (GPE).Click here for fileFile in text format. Content of the file should be inserted to the end of ffg43a2.hdb file of GROMOS96 43a2 force field for supporting non-typical amino acid residues: N-methyllysine (MLZ), protonated N-methyllysine (MLZH), N-dimethyllysine (MLY), protonated N-dimethyllysine (MLYH), N-trimethyllysine (M3L) and L-glutamyl 5-glycerylphosphorylethanolamine (GPE).Click here for file


Targeting signals direct proteins to their extra - or intracellular destination such as the plasma membrane or cellular organelles. Here we investigated the structure and function of exceptionally long signal peptides encompassing at least 40 amino acid residues. We discovered a two-domain organization (“NtraC model”) in many long signals from vertebrate precursor proteins. Accordingly, long signal peptides may contain an N-terminal domain (N-domain) and a C-terminal domain (C-domain) with different signal or targeting capabilities, separable by a presumably turn-rich transition area (tra). Individual domain functions were probed by cellular targeting experiments with fusion proteins containing parts of the long signal peptide of human membrane protein shrew-1 and secreted alkaline phosphatase as a reporter protein. As predicted, the N-domain of the fusion protein alone was shown to act as a mitochondrial targeting signal, whereas the C-domain alone functions as an export signal. Selective disruption of the transition area in the signal peptide impairs the export efficiency of the reporter protein. Altogether, the results of cellular targeting studies provide a proof-of-principle for our NtraC model and highlight the particular functional importance of the predicted transition area, which critically affects the rate of protein export. In conclusion, the NtraC approach enables the systematic detection and prediction of cryptic targeting signals present in one coherent sequence, and provides a structurally motivated basis for decoding the functional complexity of long protein targeting signals. This variability has been suggested to enable alternative functions In eukaryotes, a canonical N-terminally located protein export signal typically contains three distinguishable parts: a positively charged N-terminal section  as an example. Shrew-1 was originally isolated from an epithelial-like cell line obtained from an endometriosis biopsy in vitro targeting experiments with shrew-1.Based on proteome analysis by machine-learning systems, we propose a bipartite domain model (“NtraC” model) of long signal peptides from single-pass integral membrane proteins. According to this model, such long signal peptides may contain two separate functional domains: an N-terminal domain (“N-domain”) and a C-terminal domain (“C-domain”) traceable by a turn-rich linker area connecting both. We denote this linker element “transition area” (tra). Proof-of-principle for the validity of the NtraC domain model is provided by e.g., unusually short signals) within long signal peptides.Analysis of long signal peptides was performed in two steps: First, potential domains were predicted using a novel machine-learning technique for turn prediction SignalP 3.0TargetPwww.modlab.deSoftwareSVMTurn) Searching for long signal peptides (≥40 residues) in the UniProtKB database (release 53.2) www.modlab.deSoftwareNtraC) analysis, 185 of 296 (62%) long signal peptides obey the NtraC domain organization with a C-domain coding for an ER targeting signal  and invite the scientific community to scrutinize our NtraC domain model using this prediction server.We provide a public web service for NtraC analysis of amino acid sequences . Among these, the only experimentally validated TMS is the one of shrew-1 Summarizing, we identified a class of long signal peptides distinguished by the NtraC domain architecture. This structural and functional organization is present in signal peptides of many single-pass membrane proteins. For further study, we selected one of these proteins, human shrew-1 as an example.Based on the theoretical analysis described in the previous paragraph, we used secreted alkaline phosphatase (SEAP) as a reporter protein in order to probe the targeting capacity of the predicted domains of shrew-1's signal peptide. The SEAP reporter system allows for the exchange of the intrinsic signal peptide by other potential signal peptide sequences, which can then be tested for biological activity SignalP 3.0 probability  = 0.95) is divided into three domains: It contains an N-domain (residues 1–19) and a C-domain (residues 20–43) connected by the transition area (residues 16–24). The C-domain is predicted as a standard secretion signal containing an n-, h-, and c-region , whereas the N-domain receives a prediction as a mitochondrial transit peptide (TargetP probability  = 0.3).According to the NtraC model, the shrew-1 signal peptide . Interestingly, no further β-turns were found in the remainder of the signal peptide. The position of the turns appears to be evolutionary conserved among different species, as shown by a multiple sequence alignment of seven vertebrate shrew-1 homologues, suggesting a fundamental functional importance of this region Suppl. .To functionally test the predicted signal peptide domains, six constructs coding for different SEAP fusion proteins were devised . They weC-SEAPΔSP) alone is able to direct SEAP fusion protein to the supernatant. The N-domain (SHN-SEAPΔSP) alone does not have this targeting capacity. The same holds for the whole cell lysates (As shown in ΔSP), SEAP activity in both the supernatant and whole cell lysates of SHC-SEAPΔSP transfected cells was decreased to about one third. This implies that the full-length signal peptide is required for full export efficiency, but basic targeting information is encoded in the C-domain of the long signal peptide.Compared to full length shrew-1 signal peptide (SH-SEAPN-SEAPΔSP protein is not N-glycosylated (lanes 7 and 8), whereas SHC-SEAPΔSP and SH-SESPΔSP contain an N-glycosylated SEAP population . We conclude that SHN-SEAPΔSP was not transported into the ER. It is noteworthy that SHN-SEAPΔSP was found in two non-glycosylated bands (lanes 7 and 8), indicating the existence of two populations with different molecular mass. The position of the bands is in line with the idea that the upper band contains the N-domain of the signal peptide, which might have been cleaved off in the faster migrating protein (lower band) by some non-ER protease activity.Notably, both fusion proteins were detectable by Western blotting . This raN-SEAPΔSP fusion protein is able to target to mitochondria, as predicted by our sequence analysis (vide supra). Therefore, we analyzed mitochondrial localization of SHN-SEAPΔSP. HEK 293T cells were transfected with either SHN-SEAPΔSP or SHC-SEAPΔSP, and mitochondria were isolated by differential centrifugation followed by density gradient centrifugation. Cytosolic (cyto) and ER fractions obtained by differential centrifugation were positive for GAPDH as a cytosolic marker protein, or grp94 as an ER marker, and negative for cytochrome C as a mitochondrial marker . This experimental observation is in perfect agreement with the computational prediction.SHfraction  and in afraction . In contfraction . A diffefraction . This obfraction . Most imThe results presented so far show that the C-domain is sufficient for secretion of SEAP fusion protein, whereas the N-domain has no ER translocation capacity, but rather accommodates a mitochondrial targeting activity. However, when compared to the full length signal sequence the C-domain exhibits a decreased secretion activity. This observation gave rise to the question whether the transition area (residues 16–24) influences the efficiency of ER translocation.G18I-SEAPΔSP) which was predicted to prevent the formation of the first turn in the transition domain. In the second construct, we deleted the first four amino acids with the highest turn forming potential (SHΔWPGR-SEAPΔSP) of the predicted transition domain. In the third construct, we deleted the first four amino acids of the transition area and introduced additional substitutions in the remaining four amino acids in order to completely disrupt the transition area (SHΔWPGR/mut-SEAPΔSP)  acts as some kind of separation unit between two sequence domains with different targeting capacity. Results of cellular targeting studies highlight the functional importance of the transition area. A minimal interpretation is that it affects ER translocation of the reporter protein.e.g. microsomal CYP2E1 The N-domain (residues 1–19) was able to act as a mitochondrial targeting signal in our experiments. Similar observations have been made for other proteins containing consecutive “tandem” signals rather than “cryptic” signals as described by the NtraC model. The transmembrane glycoprotein nicastrin, which is an essential component of gamma-secretase Mitochondrial targeting of shrew-1 and other proteins containing NtraC-organized long signals may not occur constitutively but in a regulated manner or only under cellular stress, and our results indicate that the mitochondrial targeting signal (N-domain) and the ER targeting signal (C-domain) are not sequentially processed. The N-domain of shrew-1 harbors no ER translocation activity, but is able to mediate mitochondrial targeting. We wish to stress that this activity has been proven for the isolated N-domain in the context of the experimental setup used in the present study, and it needs further investigation to determine the conditions under which this activity is found in the context of the full-length signal peptide. Possibly this cryptic activity is revealed under certain physiological situations only.As an extension to the already known tandem signals like in the nicastrin or CYP2E1 precursors Due to its amphipathic nature, we further speculate that the N-domain might be involved in dimerization or stabilization of shrew-1 in the plasma membrane or interaction with other proteins via the ER, but not as effective as the full-length signal peptide. Most strikingly, the transition area which was first predicted to only link the N- to C-domain, turned out to be essential for the full ER translocation activity of the C-domain. It is noteworthy that the transition area is the only part of the long signal peptides predicted to predominantly contain β-turns. Thus, turn formation seems to be not only a structural element separating the N- and C-domains, but a decisive feature of long signal peptides supporting the ER translocation activity of the C-domain. The NtraC model thereby explains earlier observations made for interleukin-15, which is subjected to different export rates depending on the length of its signal peptide The C-domain is sufficient for protein export Our model also provides a rational explanation for membrane targeting of bacterial autotransporters, which possess long signal peptides: These are in accordance with our NtraC model, where the C-domain alone is sufficient for transport to the inner membrane but for proper processing the complete signal peptide is required Constructs were generated by PCR Suppl. .5 cells were seeded per 12 cm2 of culture dish and transfected with 3 µg DNA 24 h later by using Magnet Assisted Transfection  according to the manufacturer's instructions.HEK 293T  were cultured in Dulbecco's Modified Eagle Medium  with 10% fetal calf serum  and 1% penicillin/streptomycin . 6×10SEAP activity assays were performed according to 2O2 for horseradish peroxidase.After collection of supernatant for SEAP assays, cells were washed with PBS and lysed with 100 µl RIPA buffer  containing proteinase inhibitor cocktail Complete  at 4°C for 30 min. Lysates were cleared by centrifugation in a microcentrifuge at 4°C for 5 min. Where indicated, cell lysates were treated with PNGase F which removes N-glycans according to the manufacturer's instructions . For immunoblotting, 20 µg of protein from each cell lysate was separated in a 6% SDS PAA-gel. Protein blots were incubated with rabbit polyclonal anti-myc antibody  diluted in TBST . Glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was probed with mouse monoclonal anti-GAPDH antibody , cytochrome c with mouse monoclonal anti-cytochrome c antibody  and Grp94 with rat monoclonal anti-Grp94 antibody . Secondary alkaline phosphatase-conjugated goat anti-rabbit antibody, horseradish peroxidase-conjugated goat anti-rabbit, horseradish peroxidase conjugated goat anti-mouse antibody and horseradish peroxidase conjugated goat anti-rat antibody  were used for detection of first antibodies. Enzyme substrates were NBT/BCIP  for alkaline phosphatase or a solution of luminol (2.5 mM), p-coumaric acid (0.4 mM), Tris-HCl, pH 8.5 (100 mM) and 0.009% HThe densitometric analysis of the Western blots was performed with Image J (Scion). The densities of the corresponding bands on the blot were measured and the ratio of the upper band to the lower band of each construct was calculated.24 hours after transfection of HEK 293T cells mitochondria were isolated with the Qproteome Mitochondria Isolation Kit  according to the manufacturer's instructions. Briefly, after removal of nuclei, cell debris, cytosolic and microsomal cell fractions, the mitochondria pellet was resuspended in 0.5 M sucrose buffer , layered on a 1–2 M sucrose gradient  and centrifuged for 2 h at 25,000 rpm. The mitochondrial band was collected, diluted with 2 volumes of 1 mM EDTA, 10 mM Tris-HCl, pH 7.4 buffer and pelleted by centrifugation at 20,000×g for 15 min. 20 µg of protein of each fraction was loaded on a 10% PAA-gel and separated by SDS-PAGE.Table S1Vertebrate signal peptides >40 amino acids, which are predicted to be NtraC organized but differ in their domain capacity from shrew-1. Underlined residues are predicted turns belonging to the T-domain(0.17 MB DOC)Click here for additional data file.Figure S1Multiple sequence alignment of the signal peptides of shrew-1 homologues(0.37 MB DOC)Click here for additional data file.Text S1Oligonucleotides used for cloning of SEAP fusion constructs.(0.06 MB DOC)Click here for additional data file.