First-generation interaction maps of Src homology 2 (SH2) domains with receptor tyrosine kinase (RTK) phosphosites have previously been generated using protein microarray (PM) technologies. Here, we developed a large-scale fluorescence polarization (FP) methodology that was able to characterize interactions between SH2 domains and ErbB receptor phosphosites with higher fidelity and sensitivity than was previously achieved with PMs. We used the FP assay to query the interaction of synthetic phosphopeptides corresponding to 89 ErbB receptor intracellular tyrosine sites against 93 human SH2 domains and 2 phosphotyrosine binding (PTB) domains. From 358,944 polarization measurements, the affinities for 1,405 unique biological interactions were determined, 83% of which are novel. In contrast to data from previous reports, our analyses suggested that ErbB2 was not more promiscuous than the other ErbB receptors. Our results showed that each receptor displays unique preferences in the affinity and location of recruited SH2 domains that may contribute to differences in downstream signaling potential. ErbB1 was enriched versus the other receptors for recruitment of domains from RAS GEFs whereas ErbB2 was enriched for recruitment of domains from tyrosine and phosphatidyl inositol phosphatases. ErbB3, the kinase inactive ErbB receptor family member, was predictably enriched for recruitment of domains from phosphatidyl inositol kinases and surprisingly, was enriched for recruitment of domains from tyrosine kinases, cytoskeletal regulatory proteins, and RHO GEFs but depleted for recruitment of domains from phosphatidyl inositol phosphatases. Many novel interactions were also observed with phosphopeptides corresponding to ErbB receptor tyrosines not previously reported to be phosphorylated by mass spectrometry, suggesting the existence of many biologically relevant RTK sites that may be phosphorylated but below the detection threshold of standard mass spectrometry procedures. This dataset represents a rich source of testable hypotheses regarding the biological mechanisms of ErbB receptors. Signaling proteins containing Src homology 2 (SH2) The human ErbB family Previous studies have characterized the regulatory role of a small subset of SH2 domains, including those from c-Src (SRC) Generalized binding preferences for most SH2 domains have previously been defined through screening approaches using degenerate peptide libraries Ds) weaker than 2 µM because of technical limitations related to concentration-dependent aggregation of rhodamine-labeled peptides Ds much weaker than 2 µM. For example, the interaction of the c-Src SH2 domain with physiological PDGFR phosphosites has been estimated at approximately 4 µM Previous phosphopeptide query sets have been heavily biased towards ErbB receptor auto-phosphorylation sites, which are likely to represent the sites of highest phosphorylation stoichiometry following ErbB activation and have been most often reported by mass spectrometry based literature reports. Additionally, PMs were not able to accurately or reproducibly quantify interactions with midpoint dissociation constants (KFluorescence polarization (FP) has previously been used to characterize relatively weak protein interactions including the binding of fluorescent peptides to the c-Src SH2 domain A more comprehensive description of methods is provided in the D≤1 µM. Where multiple SH2 domains were contained in a single gene, the tandem protein was included in our analysis with all internal amino acids linking the domains even if the percentage of monomeric tandem SH2 domains was less than 50%.The cloning of 109 SH2 and 44 PTB domains in the human genome is previously described Peptides were synthesized on a Symphony® 12 channel multiplex peptide synthesizer  in either N,N-dimethylformamide (DMF)  or N-Methyl-2-Pyrrolidone (NMP)  on the solid phase at a 50 µmol scale using standard Fmoc chemistry. Standard amino acids were coupled twice for 30 minutes at five-fold molar excess with the exception of leucine, valine, isoleucine, threonine and tryptophan, which were coupled three times for 45 minutes; phosphotyrosine was coupled twice for three hours; standard amino acids were coupled twice for one hour following phosphotyrosine addition. In most cases, amino acids were activated in situ with 0.9 equivalents of 2-(1H-benzotriazole-1-yl)-1,1,3,3-tetramethyluronium hexafluorophosphate (HBTU), one equivalent of 1-Hydroxybenzotriazole (HOBT), and two equivalents of N,N-diisopropylethylamine (DIPEA)  and coupled at room temperature. Phosphotyrosine was pre-activated separately for 30 minutes prior to coupling. Either standard polystyrene Wang resin (0.6 mmol/g) charged with Fmoc-Asp (OBut)  or CLEAR-Acid resin (0.4 mmol/g) charged with Fmoc-Asp (OtBu)  was used to initiate peptide chains. Following synthesis but before cleavage, peptides were N-terminally labeled twice for four hours with two equivalents of rhodamine WT  activated with equimolar amounts of HBTU, HOBT, and three equivalents of DIPEA. 5 mM DTT was added to all cysteine-containing peptides prior to peptide analysis and purification.Ds) for each protein/peptide pairing by least squares nonlinear regression. See  RhodaminZ’ factors for each putative interaction detected by FP. The Z’ factor is defined in D of that interaction. For negative controls of the free state, we incubated identical concentrations of each labeled peptide with a thioredoxin control at the same concentration as the positive control protein. All Z’ factor calculations were performed in R (http://www.R-project.org).To evaluate the suitability of a subset of binary interaction partners for examination in competitive binding assays, we determined D of the interaction (based on the FP saturation binding assay), respectively. Unlabeled competitor peptides were prepared at 11 serial 1∶3 dilutions in assay buffer; one control with no unlabeled competitor peptides was also included with each experimental set. The fluorescent peptide, protein, and unlabeled peptide mixtures were allowed to incubate for a minimum of 20 minutes in 384-well plates and polarization signals were measured using the Analyst GT multimode reader as previously described above. Inhibition curves were constructed by four-parameter log-logistic regression using the “drc” package Z’ factor positive and negative control values. IC50 values were estimated from these fits under a one-site competition binding model and Ki calculated according to the equation developed by Unlabeled phosphopeptides were synthesized with some modifications to the previously described protocol. Final fluorescent peptide and protein concentrations were kept constant at 10 nM and four times the calculated K2O. Residual water was removed by centrifugation; slides were stored under desiccation in the dark until analysis.300 pL of purified recombinant proteins and a thioredoxin tag control were spotted at a concentration of ∼40 µM in duplicate onto aldehyde-coated microscope slides  using a Nano-Plotter (NP) 2.1/E noncontact microarrayer . 16 identical microarrays per slide were printed in a 2×8 (column by row) format with distances of 9 mm between individual arrays; each array consisted of a 14×14 configuration of spots 400 µm apart. Following printing at a constant humidity level of 50%, slides were desiccated overnight and stored at −80°C. For peptide probing, array slides were transferred to 96-well microplates  and sandwiched with silicone gaskets. Prior to peptide addition, aldehyde sites were quenched with buffer B  containing 1% bovine serum albumin (BSA) (w/v) for one hour at room temperature; moderate agitation was applied in a plate shaker  to displace glycerol from the surface area of protein sample spots. After washing the arrays briefly with buffer B alone, a titration series of rhodamine-labeled peptides (whose concentration was determined by rhodamine emission at 555 nm) dissolved in buffer B was added to the PMs and incubated in the dark for one hour at room temperature with gentle agitation. The arrays were subsequently washed once with 300 µL of buffer B and once with 300 µL of ddH2+ ions. Dye-free ErbB family peptides were applied to the sensor chip in a single flow cell in solution phase (while the other flow cell was used as a reference control) and binding events were measured under equilibrium conditions. FP false positive All SPR experiments were performed using Biacore 3000  instrumentation. 6X His-tagged SH2 domain proteins were immobilized on a nitriloacetic acid (NTA) sensor chip  charged with NiDs) for each protein/peptide pair using non-linear regression .To scale up the number of interaction measurements while minimizing variation introduced by human intervention, we utilized an automated robotic platform  to transP = 2.80×10−14) and not significantly different (P>0.05) for assayed protein-peptide interactions. However, 13-mer peptides generated significantly higher maximum polarization (MaxP) levels than 18-mer peptides . Because increased maximal polarization amplitudes would likely enable higher sensitivity and more robust determination of interaction affinities, only 13-mers were synthesized for the remaining 61 peptides.In our previous study For this interaction analysis, we synthesized and queried the interaction of phosphopeptides corresponding to 89 of 92 ErbB family intracellular tyrosines with 93 SH2 domains. Because nine peptide sequences share 100% identity between the receptors in the 4 to 7 amino acids C-terminal to the phosphorylated tyrosine, our peptide set consisted of 81 phosphopeptides corresponding to the 89 unique tyrosine containing sequences . This peD of these detected interactions was 4.84 µM, an affinity much lower than the threshold filter of 2 µM used previously for PMs.The ability of the FP method to detect weaker interactions than PMs, coupled with a more comprehensive peptide query set, revealed a much larger interaction set than has previously been described . In totaTo further assess the accuracy of interactions quantified by the automated FP assay, the surface plasmon resonance assay (SPR) was used to cross-validate a subset of FP interaction positive and negative hits. Because of the lower throughput nature of the SPR assay, a matrix of 63 interactions consisting of 12 peptides and seven proteins was randomly selected for this analysis . The SPRUsing interaction data from the SPR assay as a low throughput gold standard approach in vivo interactions were identified by our FP assay. Notably, our assay predicted more than 200 novel protein interactions (among greater than 1100 novel peptide-protein interactions).We also compared the FP-derived interactions between ErbB family members and the assayed proteins to a set of 44 cellular interactions found in the iRefWeb, BioGRID, HPRD, and IntAct resources see . While tDs (from ∼100 nM –6 µM) were then selected as a base set for subsequent competitive displacement analysis (D<20 µM competitively displaced labeled peptides (D >20 µM) resulted in negligible competition. For example, ErbB3-pY1197 (Peptide 4) competitively displaced the labeled version of itself as well as ErbB3-pY1289 (Peptide 7) from the PTK6 SH2 domain  than suggested by previous PM studies and a much greater number of moderate-to-low affinity interactions (20> KD >2 µM) (D≤0.3 µM) of PM interactions was over 1.5 times more likely to be identified by FP as compared to the weakest quantile (KD≥1.28 µM) .We next compared the interaction overlap between previous PM datasets and the FP interaction matrix. For these analyses, we limited our comparison to the most recent estimate of 448 protein interactions representing 462 unique ErbB phosphotyrosine site interactions stemming from three previously published PM studies [34]–[36]D >2 µM) . We alsoD >2 µM) . The tigD  of PMs by identifying the proportion of PM interactions that were not detected by FP. 265/448 (59%) interactions detected by PMs were not called as interactions in our FP data set. To estimate the true FNR of PMs, we next established a set of 235 “high-confidence” FP interactions whose observed replication rates significantly exceeded the expected rates given their K2 test P = 0.76). However, when interaction data was assessed from PMs fabricated at different times, the reproducibility of PMs was much less compared to data taken from FP performed on different days  thus indicating substantial batch to batch variability in the PM methodology and that technical replicates are critical to obtain an estimate of the veracity of interactions and non-interactions called by PMs.In order to more systematically compare the technical reproducibility of the FP and PM methods, we performed 39 in-house PMs using 12 phosphopeptides. We then compared the reproducibility of identified interactions from PMs with the reproducibility from the FP assay . A similD range) to one central phosphosite in addition to differentially recruiting these domains with lower affinity to receptor sites at other locations. VAV GEF family SH2 domains were recruited with highest affinity to one central and one C-terminal site of ErbB3 (in nM KD range), and to centrally located phosphosites of ErbB1, ErbB2, and ErbB4. SH2 domains from SRC and other tyrosine kinase genes were recruited with moderate affinity (in 1 µM KD range) to both N- and C-terminal sites of ErbB3 and to similar locations but at lower affinity to ErbB4; these domains were recruited to C-terminal phosphosites of ErbB1 and ErbB2 with much lower affinity (in 10–20 µM KD range). GRB2 and GRAP2 SH2 domains were recruited with moderate affinity (in the 1 µM KD range) to a single C-terminal phosphosite of ErbB4, to one membrane proximal and one C-terminal phosphosite within ErbB2, and to two C-terminal phosphosites within ErbB1. We speculate that the locations of domain recruitment to ErbB receptors may be important in the assembly of receptor specific signaling modules.Analysis of the interaction dataset revealed trends in the recruitment locations of different SH2 domains with ErbB receptors. Although some SH2 domains like those from PLCG1 were recruited to many phosphosites in each receptor, others were recruited to only a small number of phosphosites located in the same general location in each ErbB family member . For exaDs because of aggregation of a subset of rhodamine labeled peptides when titrated at concentrations exceeding 1 µM. Notably, although ErbB4 has more total intracellular tyrosines compared to the other receptors , it had the most peptides that showed no interactions with any SH2 domains, perhaps owing to its additional transcription related functions in the nucleus Previous PM-based studies have suggested that the oncogenic potential of ErbB2 arises because of its unique ability to promiscuously recruit many different SH2 domains with low affinity In order to further define and classify commonalities and differences among the ErbB family, the relative binding free energy (FE) of each SH2 domain was summed across all phosphosites on each receptor relative to the assay detection threshold of 20 µM according to Ds ≤100 nM for Y1197 and Y1260) existed exclusively on ErbB3, consistent with a previous report demonstrating the ability of peptides derived from specifically ErbB3-pY1197 and -pY1260 to capture GRB7 from cell lysates After summation, the common and distinct recruitment capacity of each ErbB receptor for SH2 domains was more readily apparent . ErbB1 aStrikingly, although PIKR3-C terminal SH2 domains bound with substantial FE to all ErbB receptors, the PIK3R1 and PIK3R2 C-terminal SH2 domains were selective and bound ErbB3 with 4- to 50-fold higher FE than ErbB1, ErbB2, and ErbB4. In the context of a cell, the tandem SH2 domains of PIK3R3 would be expected to bind with relatively high affinity to all ErbB receptors through avidity gained by binding with both domains. Conversely, PIK3R1 and PIK3R2 would display avidity for only ErbB3 and not the other receptors because their C-terminal domains bind only ErbB3 sites with high affinity. The highest affinity PIK3R recruitment sites on ErbB1, ErbB2, and ErbB4 that were determined by FP  were not previously examined by PMs. The C-terminal PIK3R1 and PIK3R2 SH2 domains rarely interacted with any phosphopeptides when probed with PMs indicating that the domains may have potentially lost functionality when immobilized on the microarray surface.In addition to displaying differences in total affinity of recruitment of domains, we found that some receptors were enriched for total numbers of binding sites versus other receptors. We found that ErbB1 was enriched for total SHC1 PTB, and PLCG1 and TNS family SH2 domains versus the other receptors but depleted versus the other receptors for binding sites for SRC and NCK family SH2 domains . Howeverhttp://www.geneontology.org/; To better understand SH2 domain recruitment by ErbB receptors in the context of receptor biology, we clustered the domains into groups based on several key molecular function categories such as cell proliferation, migration, and survival  [48] (htP = 0.01) and phospholipase (P = 0.03) binding sites. ErbB3 was enriched for cytoskeletal regulation (P = 0.049), kinase (P = 0.001), phosphatidyl inositol kinase (P<1×10−5), and signal regulation (P = 0.002) binding sites. ErbB3 also recruited significantly more unique tyrosine kinases than the other receptors (P = 0.04): 20 for ErbB3 versus 11, 16, and 17 by ErbB1, ErbB2, and ErbB4, respectively.We also examined whether a significant enrichment existed for the total number of binding sites contained by each receptor for domains from particular molecular function categories -Tab2. ErIn the first phase of this study, we rigorously assessed several technical parameters of the FP method including analysis of false positive and false negative rates through cross-methodological comparison with the SPR assay. We additionally validated the binding pocket specificity of interactions identified via the FP method by performing competition binding analyses with unlabeled phosphopeptides. Our study captured 1405 unique biological interactions, 1169 (83.2%) of which were not described by previous PM studies. This increase in interactions captured by the FP method was likely attributable to: 1) querying a 50% larger peptide set than previous studies; and 2) the high rate of true positives and low rate of false negatives of the FP method relative to PMs or other commonly used high-throughput protein-protein interaction analysis methods. Our analysis of PMs indicated a higher rate of false positives and false negatives than has been previously reported. Of 656 SH2-phosphopeptide interactions reported to date by PMs, only 236 were confirmed by the FP assay.Ds weaker than about 2 µM because of the tendency of rhodamine-labeled peptides to aggregate resulting in visual artifacts on the array surface We speculate that the following reasons may contribute to the higher rate of PM false negatives. Firstly, the microarray printing of PM slides typically requires a period of about ten hours followed by an additional several hours of dehydration prior to freezing. These steps may potentially lead to reduced functionality of printed proteins. Secondly, immobilization of SH2 domains via amine chemistry on aldehyde-coated slides may further reduce the functionality of printed proteins. Lastly, PM platforms cannot accurately measure peptide interactions with KWith regard to PM false positives, we suspect that non-specific interactions may arise from denaturation of proteins during surface immobilization, incomplete solubilization of either aggregated or precipitated peptides, and from non-specific interactions between peptides and microarray slide blocking agents such as bovine serum albumin or gelatin. The PM method requires that microarrays be washed with sufficient rigor to remove non-specifically bound peptides from proteins and glass without dissociating peptides specifically bound to SH2 protein spots. Peptides that bind more weakly to particular proteins are more likely to be lost during the wash procedure than high affinity binders because of higher off rates and lower total occupancy of protein spots at a given concentration. At sufficiently high concentrations, most peptides will interact non-specifically with blocking agents. Noise generated from such interactions places practical limits on the strength of interactions that can be queried with PMs.False positive interactions may also result from disulfide bonding of SH2 domains with one another and/or receptor peptides leading to a reduction in the kinetics by which non-specifically bound peptides unbind and wash away into bulk solution. Previous SH2 PM experiments have been performed in the absence of reducing agents, whereas FP experiments in this study were performed with dithiothreitol (DTT). Since the majority of intra-cellular compartments are reducing in nature, we hypothesized that the inclusion of a reducing agent would represent a more physiologically relevant environment for assaying SH2-ErbB interactions. When we included DTT with peptides on PMs, we observed no loss in Cy5 signal originating from immobilized protein domains. However, we saw a dramatic reduction in rhodamine signal from bound peptides washed under identical conditions . AlthougIn the second phase of this study, we explicitly compared the reproducibility of data obtained by the FP method with that obtained by PMs. Publicly available PM datasets have aggregated new interaction data with old data without indicating which was new or old Ds semi-quantitative estimates of the true values. For these reasons, we favor an approach whereby each protein is expressed in multiple batches and independently tested for interaction with phosphopeptides multiple times. The average affinities can then be examined with a better perspective regarding protein functionality and assay variability.We estimate that the greatest source of variability in the FP assay, which similarly contributes to variability in the PM assay, is batch-specific differences in protein functionality. In our FP study, the amount of protein obtained for some domains from a single 500 ml bacterial expression culture was often not sufficient for querying all peptides in our dataset multiple times. This problem could be mitigated to a certain extent by making larger batches of recombinant protein. However, difficulty in estimating the absolute concentrations and relative functionality of all of the protein domains from a single expression would still render the resulting apparent K2 test P = 0.052). Indeed, membrane-proximal phosphosites such as ErbB1-pY813, ErbB2-pY685, ErbB3-pY789, ErbB3-pY823, and ErbB4-pY906 reproducibly interacted at high affinity with many SH2 domains, but have not previously been reported to be phosphorylated , many hetero-phosphorylation sites–those that require the kinase activity of a non-receptor tyrosine kinase–have seldom been reported in the literature (less than five times) . The facD weaker than 2 µM . A representative set of all assayed proteins used in this study is displayed.(PDF)Click here for additional data file.Figure S2Short-form peptides produce higher polarization values than long-form peptides. (A) Heatmaps depict apparent midpoint dissociation constants (KDs) of SH2 and PTB domains with indicated 18-mer (upper panel) and 13-mer (lower panel) peptides. KDs are color coded by affinity . Lower-case “d” denotes aspartic acid (Asp) residue pre-charged on the peptide synthesis resin and not a naturally-occurring Asp. (B) Polarization values obtained by FP were fit to C) Side-by-side 13-mer and 18-mer bean plots of Pmax values.(PDF)Click here for additional data file.Figure S3Histogram depicting how many times each peptide-protein interaction was tested across all six automated FP runs in our assay.(PDF)Click here for additional data file.Figure S4Heatmaps depicting maximum polarization values of interactions of SH2 and PTB domains with all ErbB family phosphopeptides. Polarization values obtained by FP were fit to (PDF)Click here for additional data file.Figure S5SH2 domain recruitment potential of the ErbB family as previously determined by protein microarrays. Color-coded heat maps (see legend) represent apparent dissociation constants (KDs) for protein microarray interactions between SH2/PTB domains and phosphopeptides representing potential ErbB1, ErbB2, ErbB3 and ErbB4 phosphotyrosine sites. Binding strength is color-coded as indicated on the legend. Homologous peptides with identical amino acid sequences at the +1 to the +4 position relative to the phosphotyrosine (X) are marked with an asterisk followed by the number of the homologous receptor with sequences indicated. Lower-case “d” denotes the aspartic acid (Asp) residue pre-charged on the peptide synthesis resin and not a naturally-occurring Asp. Rows of the heatmaps for peptides that have no previously reported protein microarray interactions are grayed out to indicate that no experiments were performed to confirm or deny positive or negative interactions from these peptides.(PDF)Click here for additional data file.Figure S6Overlap between protein microarrays and fluorescence polarization approaches. Interactions detected by protein microarrays (PMs) in previous studies are plotted with interactions detected by the fluorescence polarization (FP) assay in this study. Interactions detected by PMs alone are colored green. Interactions detected by FP alone are colored red. Interactions detected by both platforms are colored in yellow. Homologous peptides with identical amino acid sequences at the +1 to the +4 position relative to the phosphotyrosine (X) are marked with an asterisk followed by the number of the homologous receptor with sequences indicated. Lower-case “d” denotes the aspartic acid (Asp) residue pre-charged on the peptide synthesis resin and not a naturally-occurring Asp. Peptides that were unable to be synthesized (NS) and those that were queried but resulted in no positive interactions (NI) are indicated. Rows of the heatmaps for these peptides are grayed out to indicate that neither FP nor PM could experimentally confirm or deny positive or negative interactions from these peptides.(PDF)Click here for additional data file.Figure S7Fluorescence polarization replication probability as a function of the strength of interaction as estimated by protein microarrays. 448 interactions previously detected by protein microarrays were binned into 8 groups based on their interaction strengths and the proportion of interactions that were detected at least once by FP calculated.(PDF)Click here for additional data file.Figure S8False positive rate (FPR) and false negative rate (FNR) as a function of the probability of each interaction calculated by comparing the observed versus the expected replication rate for an interaction given its strength. As expected, filtering for “high-confidence” interactions (P>0.5) gives a low FPR at the expense of greatly inflating the FNR.(PDF)Click here for additional data file.Figure S9Characterization of unique and overlapping SH2 domain recruitment patterns by individual ErbB receptors for high-confidence interactions only.(A) Four-way Venn diagram depicts SH2 domain interactions shared by or exclusive to ErbB1, 2, 3, and 4. (B) SH2 recruitment potential of EGFR family members at different affinity thresholds. The total number of unique SH2 and PTB domains recruited over a range of affinity thresholds are depicted for each receptor.(PDF)Click here for additional data file.Figure S1095% confidence intervals for total free binding energy for each receptor for each molecular function. Relative binding free energies of interactions were summed across all domains contained from a particular function. Confidence intervals were calculated by summing ΔΔG +/−1.96 standard deviations derived from the distribution of KD estimates of individual interaction replicates.(PDF)Click here for additional data file.Figure S11Inclusion of DTT causes dramatic reduction of intensities of spots on protein microarrays following probing with both cysteine and non-cysteine containing phosphopeptides. Shown are blocks of printed SH2 and PTB domains probed with 1 µM concentrations of indicated fluorescent ErbB peptide solutions  containing 0 mM  or 5 mM DTT . Red spots (upper panels) indicate printed SH2 domains which were printed with trace amounts of Cy5-labeled bovine serum albumin for spot finding purposes during microarray analysis. Rhodamine-labeled peptides bound to arrayed SH2 domains are green in color (lower panels). Spot identities for several SH2 domains are indicated for interactions observed probing with the peptide derived from ErbB2-pY1139 (upper left quadrant of images).(PDF)Click here for additional data file.Figure S12SH2 domain recruitment potential of the ErbB family as determined by protein microarrays in the absence of DTT. Color-coded heat maps (see legend) represent apparent dissociation constants (KDs) for protein microarray interactions between SH2/PTB domains and phosphopeptides representing all potential ErbB1, ErbB2, ErbB3, and ErbB4 phosphotyrosine sites. Binding strength is color coded as indicated. Homologous ErbB peptides with identical amino acid residues from +1 to the +4 position relative to the phosphotyrosine (X) are indicated with an asterisk followed by the number (in order of occurrence) of the homologous receptor. Sequences of peptides used are indicated for each homologous receptor site. Lower-case “d” denotes the aspartic acid (Asp) residue pre-charged on the peptide-synthesis resin and not a naturally-occurring Asp. Peptides that resulted in no positive interactions are designated “NI”; peptides not synthesized for PMs are designated “NS”. Rows of the heatmaps for these peptides are grayed out to indicate that our FP assay could neither confirm nor deny positive or negative interactions from these peptides.(PDF)Click here for additional data file.Figure S13SH2 domain recruitment potential of the ErbB family as determined by protein microarrays in the presence of DTT. Color-coded heat maps (see legend) represent apparent dissociation constants (KDs) for protein microarray interactions between SH2/PTB domains and phosphopeptides representing all potential ErbB1, ErbB2, ErbB3, and ErbB4 phosphotyrosine sites. Binding strength is color coded as indicated. Homologous ErbB peptides with identical amino acid residues from +1 to the +4 position relative to the phosphotyrosine (X) are indicated with an asterisk followed by the number (in order of occurrence) of the homologous receptor. Sequences of peptides used are indicated for each homologous receptor site. Lower-case “d” denotes the aspartic acid (Asp) residue pre-charged on the peptide synthesis resin and not a naturally-occurring Asp. Peptides that resulted in no positive interactions are designated “NI”; peptides not synthesized for PMs are designated “NS”. Rows of the heatmaps for these peptides are grayed out to indicate that our FP assay could neither confirm nor deny positive or negative interactions from these peptides.(PDF)Click here for additional data file.Figure S14High confidence SH2 domain recruitment potential of the ErbB family as determined by high-throughput fluorescence polarization (HT-FP). Color-coded heat maps represent apparent dissociation constants (KDs) for FP interactions between SH2/PTB domains and phosphopeptides representing all potential ErbB1, ErbB2, ErbB3, and ErbB4 phosphotyrosine sites. Homologous ErbB peptides with identical amino acid residues from +1 to the +4 position relative to the phosphotyrosine (X) are indicated with an asterisk followed by the number (in order of occurrence) of the homologous receptor site. Sequences of peptides used are indicated for each homologous receptor site. Lower-case “d” denotes the aspartic acid (Asp) residue that was pre-charged on the peptide synthesis resin and not a naturally-occurring Asp. Peptides that resulted in no positive interactions are designated “NI”; peptides that were unable to be synthesized are designated “NS”. Rows of the heatmaps for these peptides are grayed out to indicate that our FP assay could neither confirm nor deny positive or negative interactions from these peptides.(PDF)Click here for additional data file.Figure S15FP technical reproducibility and interaction number as a function of the strength of the interaction.(A) The probability of an interaction detected by FP being identified in subsequent runs. (B) Histogram depicts the number of interactions identified at each affinity threshold in our FP assay.(PDF)Click here for additional data file.Figure S16FP determined SH2 domain recruitment activity by ErbB receptors as a product of free energy potential. Color-coded heat maps (see legend) depict free energy contributions for FP interactions between SH2/PTB domains and phosphopeptides representing all potential ErbB1, ErbB2, ErbB3, and ErbB4 phosphotyrosine sites. Binding strength is color coded as indicated and represents free energy as a percentage of total recruitment potential of an SH2/PTB across all ErbB peptides for a particular receptor. Homologous ErbB peptides with identical amino acid residues from +1 to the +4 position relative to the phosphotyrosine (X) are indicated with an asterisk followed by the number (in order of occurrence) of the homologous receptor. Sequences of peptides used are indicated for each homologous receptor site. Lower-case “d”denotes the aspartic acid (Asp) residue pre-charged on the peptide synthesis resin and not a naturally occurring Asp. Peptides that resulted in no positive interactions are designated “NI”; peptides that were unable to be synthesized are designated “NS”. Rows of the heatmaps for these peptides are grayed out to indicate that our FP assay could neither confirm nor deny positive or negative interactions for these peptides.(PDF)Click here for additional data file.Table S1Recombinant Src Homology 2 domains used in FP assay.(XLS)Click here for additional data file.Table S2Peptides synthesized for fluorescence polarization assay.(XLS)Click here for additional data file.Table S3Literature reports of ErbB phosphorylated tyrosines.(XLSX)Click here for additional data file.Table S4Fluorescence polarization-derived peptide-protein interaction data.(XLSX)Click here for additional data file.Table S5Sequences and significant interactions of non-phosphorylated peptides tested.(XLSX)Click here for additional data file.Table S6Summary of all original and most recently reported protein microarray-derived interactions.(XLSX)Click here for additional data file.Table S7SPR and FP cross-validation results.(XLSX)Click here for additional data file.Table S8Literature-based validation of ErbB protein-protein interactions.(XLSX)Click here for additional data file.Table S9Z’ factor values.(XLSX)Click here for additional data file.Table S10Peptide competition assay data.(XLSX)Click here for additional data file.Table S11High confidence fluorescence polarization data used to compare to protein microarray-derived interactions.(XLSX)Click here for additional data file.Table S12In-house protein microarray replicate assays.(XLSX)Click here for additional data file.Table S13Sum of binding free energies  of SH2 domains in ErbB receptors.(XLSX)Click here for additional data file.Table S14Significant enrichment or depletion of binding sites on each ErbB receptor for SH2 domains and gene ontology groups (permutation P<0.05).(XLSX)Click here for additional data file.Table S15Molecular function classes of SH2 domains.(XLSX)Click here for additional data file.Table S16Average recruitment potential for each gene ontological class as a function of relative binding free energy .(XLSX)Click here for additional data file.Table S17Peptides synthesized for in-house protein microarrays.(XLSX)Click here for additional data file.Methods SIA detailed description of all biochemical and mathematical analysis methods used in this study.(DOCX)Click here for additional data file.


Predicting protein contacts solely based on sequence information remains a challenging problem, despite the huge amount of sequence data at our disposal. Mutual Information (MI), an information theory measure, has been extensively employed and modified to identify residues within a protein (intra-protein) that are in contact. More recently MI and its variants have also been used in the prediction of contacts between proteins (inter-protein).Here we assess the predictive power of MI and variants for domain-domain contact prediction. We test original MI and these variants, which are called MIp, MIc and ZNMI, on 40 domain-domain test cases containing 10,753 sequences. We also propose and evaluate two new versions of MI that consider triangles of residues and the physiochemical properties of the amino acids, respectively.We found that all versions of MI are skewed towards predicting surface residues. Since domain-domain contacts are on the surface of each domain, we considered only surface residues when attempting to predict contacts. Our analysis shows that MIc is the best current MI domain-domain contact predictor. At 20% recall MIc achieved a precision of 44.9% when only surface residues were considered. Our triangle and reduced alphabet variants of MI highlight the delicate trade-off between signal and noise in the use of MI for domain-domain contact prediction. We also examine a specific “successful” case study and demonstrate that here, when considering surface residues, even the most accurate domain-domain contact predictor, MIc, performs no better than random.All tested variants of MI are skewed towards predicting surface residues. When considering surface residues only, we find MIc to be the best current MI domain-domain contact predictor. Its performance, however, is not as good as a non-MI based contact predictor, i-Patch. Additionally, the intra-protein contact prediction capabilities of MIc outperform its domain-domain contact prediction abilities. Proteins are actors in a complex system, with their functions to a large extent defined by their interactions with other proteins. It is the size, shape and chemical properties of the residues on the surface of the protein that dictate the capacity of a protein to interact with other proteins. The ability to predict the residues involved in these interactions would help to identify specific functionality, structural constraints and even disease-causing mutations.In this paper we examine the capacity for mutual information (MI) methods to predict these contact residues between proteins by assessing their ability to predict contacts between two domains of a protein. To date, MI based methods have been extensively used to predict contacts within a protein (intra-protein)-12.MI uses a multiple sequence alignment (MSA) of homologous sequences; it measures the dependence between two columns in this alignment, with the aim of identifying correlated mutations. If two et al. to identify covarying positions in a viral peptide[i.e. the scores of MI are solely dependent on an MSA and no additional information, such as a phylogeny propensity table[MI was first applied to sequence alignments by Korber  peptide. We hypoty table, a similty table,18 and sty table,19, or ity table,21, MI aet al.[et al. attempted to remove the noise caused by entropy by dividing the MI score of a pair of columns by their joint entropy[et al. improved on this score by introducing MIp, which modified the MI value by a measure that aims to eliminate phylogenetic and entropic effects[Horner et al. have colet al.,23, whilet al. and insuet al. weaken tet al. used paret al.. Tillieret al. designed entropy. These a effects. Subsequ effects introduc effects suggesteet al.[MI has begun to be extended to predict inter-protein contact residues. Halperin et al. carried et al. evaluateet al.-11,27. Het al.,26.et al.[Following the idea of Pazos et al., here weet al.. We evalet al.. Hence wWe have also attempted to strengthen the predictive capabilities of MI by introducing two new MI variants. The first variant considers triangles of residues rather than pairs to identify contacts, with the aim of enhancing the signal, and is referred to as MI3D and MIp3D. As MIc already considers a third column in its normalising term, it was not extended to triangle scores. Our second variant is designed to reduce noise by grouping residues in the MSA into seven physiochemical categories and subsequently calculating MI. This modification is indicated by the suffix RA , and the resulting five variants are: MIRA, MI3DRA, MIpRA, MIp3DRA and MIcRA. Thus altogether we examine the domain-domain contact prediction ability of 10 MI measures: MI, MI3D, MIRA, MI3DRA, MIp, MIp3D, MIpRA, MIp3DRA, MIc and MIcRA, alongside the pipeline ZNMI.In our study we observe that all versions of MI do not perform as well as a current leading domain-domain contact predictor, i-Patch, which aFor this inter-protein MI investigation we use 40 proteins that have two domains, and treat each domain as a separate protein. Proteins are composed of one or more domains. A multidomain protein, protein A, will often use one of its domains to bind to protein B and another to bind to protein C, thus allowing protein A to perform multiple functions. Consequently, in reality protein-protein interactions are often domain-domain interactions. Hence uet al.[Unfortunately the seemingly more straightforward approach of building MSAs by finding homologous sequences of two proteins known to be in complex, and then pairing the sequences originating from the same species, is not feasible for the following reasons. Inferring protein-protein interactions across species based on sequence homology has a low level of accuracy, requiring a sequence identity of far higher than 70%. Using oet al. have shoHere we want to predict whether a residue is a contact residue or not using MI-based scores on the generated domain-domain MSAs. As MI assigns scores to pairs of columns in an MSA, first we calculate the MI score for all pairs of columns, or triangles of columns in the case of the 3D variants. To obtain a score for individual columns, each residue in all 40 test cases was assigned the maximum MI score that the residue column achieved with any other residue column in its MSA. We also tested assigning the average score of each residue column, but this resulted in a significant decrease in the performance of the MI variants. A residue is then assigned the score of its column.When calculating MI pair or triplet scores, as in previous work,25, onlyi.e. correctly picking a contact residue from the total set of residues, without any information about the proteins involved, is 17.1%. Running i-Patch[et al.’s MIp measure[For the 40 test cases employed, the probability of randomly selecting contacts,  i-Patch, a non-M i-Patch found th i-Patch, as well measure. We alsoversus buried residues on domain-domain MI contact prediction.We conjecture that the enhanced classification capabilities of i-Patch may be due to its use of residue propensities, along with its consideration of only residues on the surface of a protein when attempting to predict contacts between proteins. Consequently, we examined the effect of surface In our 40 test cases, the probability of randomly selecting a surface residue from all residues is 69.9%. Using MI, MIp and MIc on our dataset as surface residue predictors (is the highest scoring residue on the surface?), we observed that each of the measures surpassed this random classification and achieved a precision of 86.9%, 75.5% and 74.1% respectively, at 20% recall  of the HK and its interacting RR, only these MI and MIc scores were considered when examining performance. In accordance with our evaluation method on the 40 test cases, all buried residues were eliminated, as were residues corresponding to columns that had one or more gaps or an entropy of 0. This leaves us with 46 DHp residues, nine of which are contacts, and 68 RR residues, amongst which 24 are contacts.As Skerker As we have no score cut-off for predictions we check the number of correct predictions among the top nine predictions for DHp. If there was no relationship between the MI scores and contact sites, then the number of correct predictions would follow a Binomial distribution with sample size nine and probability of success 9/46. Under this model we would expect 1.76 correct predictions.For RR there are 24 contact residues. We check the number of correct predictions among the top 24 predictions. If there was no relationship between the MI scores and contact sites, then the number of correct predictions would follow a Binomial distribution with sample size 24 and probability of success 24/68. Under this model we would expect 8.47 correct predictions.The results are recorded in TableMIc is the best current MI domain-domain contact predictor. The performance of MIc on our domain-domain test cases is not as good as its intra-protein contact prediction. Its preIn order to perform the tasks in our methods, code was written in perl, MATLAB and Python, and is available upon request.http://www.stats.ox.ac.uk/research/bioinfo/resources, which in turn are based on datasets in[For this domain-domain MI investigation we use proteins that have two domains, rather than protein complexes, and treat each domain as a separate protein. In this manner we can be sure that accurate “protein-protein” pairings are used in the MSA. The MSAs are taken from, availabasets in,37-39. Tasets in of X-rayasets in. This stasets in,42 againasets in. The homasets in. The finasets in and MaxAasets in.Amongst the set of 67 protein cases available from, proteinWithin the 40 inter-domain MSAs there are non-standard amino acid entries, such as B, Z, X, * and ?. As there is no established method of processing these sequencing uncertainties, we choose to treat them as a gap, while the Brown and Brown pipeline code processes them as additional amino acids.%accessible to a 1.4Å radius water molecule are denoted as “surface” residues[For each reference structure protein in the dataset, we calculate the solvent accessibility of the residues using JOY; each doresidues. Those tEmploying this criterion on our 40 test cases, along with eliminating residue columns that have an entropy of 0 or contain a gap, leaves us with 5483 surface residues and 2364 buried residues. These numbers decline to 5362 and 2174 respectively when employing the 40 reduced alphabet MSAs, as the reduced alphabet MSAs have a greater number of columns with 0 entropy (Equation (1)). The ratios of surface to buried residues in the reduced and non-reduced alphabet sets are 2.5 and 2.3 respectively. The number of 0 entropy columns in the reduced and non-reduced alphabet set are 668 and 326 respectively, while the number of columns with one or more gaps in both sets are 3479.Residues within the binding interface of a pair of interacting domains are labelled as “contact” residues Figure. There i1.It is on the surface of the individual domain.2.It is < 4.5Å from a residue in the other domain.3.The solvent accessibility of the residue is different depending on whether the domain is viewed as a separate structural entity or whether the domain is in complex.If not all of the above criteria are met, a residue is denoted as a “non-contact” residue.Using these criteria, and once again ignoring residue columns having an entropy of 0 or a gap, leaves us with 1342 contact and 4141 non-contact surface residues, over all 40 test cases. These numbers decline to 1306 and 4056 respectively when employing the reduced alphabet on the 40 test cases. The ratio of contact to non-contact residues is 0.32 in both the reduced and non-reduced alphabet sets.Hunstandardised(J) of each column J in an MSA is calculated by Equation (1) with log denoting loge here, The entropy loge in our MI calculations so that we may compare our results with other MI investigations[J is a column in the MSA with probabilities P(J = j) for the discrete set of n amino acids jε{1,…,n}. When P(J = j) = 0 then we set P(J = j)log P(J = j) = 0. The entropy is maximal when all j are equally likely to occur, i.e.P(J = j) = 1/n andWe use igations,27. In tog1n=logn.In order to compare the entropies from different MSAs we standardise the entropy score as follows: Hunstandardised(J) is the entropy of column J in the MSA, andwhere J and K is defined as: The joint entropy of two columns J has n different residues, and column K has m different residues.where column The general MI formula is: J and K always covary, i.e.P(J = K) = 1 making thelog20 ≃ 2.9957[The MI is maximal when residues in columns ≃ 2.9957.Unstandardised MI values of 0 are omitted from any further analysis. The reason for this explained in the section titled “MI scores of 0.” The average MI and estimated standard deviation of the MI of all contact and non-contact pairs in the protein are then calculated. A “standardised MI score” is calculated as MIunstandardised is the MI of columns J and K in the MSA, andwhere MIunstandardised scores vary from 0.0 to 1.6, while the standardised MI scores range from -3.2 to 3.7.Our calculated et al. proposed a variant of MI that aims to correct for background (random and phylogenetic) noise of each pair of columns under consideration, MIp[Dunn ion, MIp . This MMIunstandardised is calculated as denoted in Equation (4). As previously, pairs involving a 0 entropy residue column or a gapped residue column, or having an MI score of 0 are ignored. APC, the average product correction, is a modification term for columns J and K in the MSA, evaluated as follows: where J,K, andwhereAs done previously for MI, MIp scores are also standardised (Equation (8)), MIpunstandardised is the MIp of columns J and K in the MSA andwhere Our MIp scores vary from 0.0 to 0.4, while the standardised MIp scores range from -3.1 to 7.0.Lee and Kim designed normalising measures that aim to reduce phylogenetic noise in MI scores. They beMIunstandardised is the MI score of the columns J and L, which is calculated as described in Equation (4). The number of columns in the MSA are denoted by n. Since the CPS is the product of two MI scores, it is then normalised by the square root of the mean of all CPS scores. Here i.e. one column from each protein, and adjust n in Equation (10) accordingly. Once again MI values of 0 are ignored, as are 0 entropy and gapped residue columns. This NCPS score is then subtracted from the corresponding MI pair score to yield Lee and Kim’s noise reduced MI variant, MIc. To adapt the NCPS for domain-domain prediction we consider only those CPS scores that refer to domain-domain column pairs, MIc scores for each protein are standardised in a manner similar to MI and MIp (Equations 5 and 8), so that MIc values from different proteins can be compared. MIcunstandardised is the MIc of columns J and K in the MSA andwhere The MIc scores calculated on our 40 test cases range from -0.02 to 0.1, while the standardised scores range from -2.4 to 7.7.MI and MIpJ from domain 1 has n different residues, column K from domain 2 has m different residues, and column L from domain 2 has s different residues. Residues in the representative protein structure, corresponding to columns K and L, should be < 4.5Å from each other in order to be considered as being on the same patch in the domain.where MSA column MIp3D is defined as MI3Dunstandardised is calculated as denoted in Equation (13) and APC3D is calculated as In this equation J,K,L, andwhereIn order to compare the 3D mutual information scores between test cases, MI3D and MIp3D scores were standardised in a manner similar to those described in Equations (5), (8) and (12), respectively. Once again MI3D values of 0 were ignored, as were 0 entropy columns and columns containing one or more gaps.et al. in their domain-domain contact predictor, i-Patch[et al. introduced Favoured and Disfavoured categories because Lysine (K) was found to be rare in protein/domain interfaces (propensity 0.66), while Arginine (R) and Histidine (H) were far more common [We grouped the 20 amino acids into the same seven physiochemical categories successfully employed by Hamer  i-Patch. These sctively).We replaced the amino acid alphabets in each MSA by their corresponding category abbreviation and recalculated MI, MIp, MIc, MI3D and MIp3D as described above. The five new MI variant scores are referred to as MIRA, MIpRA, MIcRA, MI3DRA and MIp3DRA.We choose to employ this particular set of seven physiochemical categories as it was successfully used by i-Patch in domaiFor classification, each residue in all 40 test cases was assigned the maximum MI score that its residue column achieved with any other residue column in its MSA. When the average score of each residue was assigned instead, the performance of the MI variants decreased significantly, consequently the maximum score was employed.versus negative cases, P-ROC  curves[When there is a disproportionate number of positive ) curves provide ) curves when attand TP in these equations denote the number of true positives, FP denotes the number of false positives and FN symbolises the number of false negatives.versus negative cases. For example, in FigureEach P-ROC plot contains a flat horizontal line (green) atTo test the stability of the 10 MI variant scores under minor changes in the MSA, for each test case 70% of sequences in the MSA were randomly selected and all 10 MI scores recalculated and 10 respective P-ROC curves were plotted. This sub-sampling and calculation process was repeated 100 times per test case for every MI variant. Then the average and standard error of the precision values for the 100 P-ROC curves were calculated for each MI variant. The precision values at 20% recall for each of the MI variants are listed in TableThis sub-alignment creation and MI recalculation process was only carried out on those 24 test cases that had ≥200 sequences to ensure that a minimum of 125 sequences were retained in each sub-alignment, the suggested minimum number of sequences required to reduce the stochastic noise in the MSA.i.e. a column with an entropy of 0, results in a joint entropy equivalent to the entropy of the non-fully conserved column and subsequently an MI score of 0 for that pair. Since conserved columns do not give any indication of correlated mutations, MI scores involving these columns are ignored. This is standard procedure; for example,[Pairing any MSA column with a fully conserved column, example,. The relThe authors declare that they have no competing interests.MG participated in design of the study, wrote the code used for analysis and drafted the manuscript. RH assisted with coding, participated in design of the study and edited the manuscript. GR supervised this research, directed design of this study and the statistical analysis employed, and edited the manuscript. CMD conceived this piece of work, supervised the research, directed design of the study and edited the manuscript. The final manuscript was read and approved by all authors.Figure S1. Surface versus buried prediction P-ROC curves for MI variants on the 40 test cases. A, B and C illustrate the performance of MI, MIp and MIc variants respectively when distinguishing surface from buried residues. The solid green line in all plots depicts the chance of randomly selecting surface residues, while the dashed green line indicates the probability of randomly selecting a surface residue when employing the reduced alphabet amino acid set.Click here for fileFigure S2. Contact versus non-contact prediction MCC curves for MI variants on the 40 test cases. Performance evaluation of the predictive power of MI, MIp and MIc using the Matthews Correlation Coefficient (MCC) score[C) score. A, B anClick here for file


Throughout evolution, the LIM domain has been deployed in many different domain configurations, which has led to the formation of a large and distinct group of proteins. LIM proteins are involved in relaying stimuli received at the cell surface to the nucleus in order to regulate cell structure, motility, and division. Despite their fundamental roles in cellular processes and human disease, little is known about the evolution of the LIM superclass.We have identified and characterized all known LIM domain-containing proteins in six metazoans and three non-metazoans. In addition, we performed a phylogenetic analysis on all LIM domains and, in the process, have identified a number of novel non-LIM domains and motifs in each of these proteins. Based on these results, we have formalized a classification system for LIM proteins, provided reasonable timing for class and family origin events; and identified lineage-specific loss events. Our analysis is the first detailed description of the full set of LIM proteins from the non-bilaterian species examined in this study.Six of the 14 LIM classes originated in the stem lineage of the Metazoa. The expansion of the LIM superclass at the base of the Metazoa undoubtedly contributed to the increase in subcellular complexity required for the transition from a unicellular to multicellular lifestyle and, as such, was a critically important event in the history of animal multicellularity. Caenorhabditis elegansCaenorhabditis elegansLIM is an ancient eukaryotic protein domain that originated prior to the last common ancestor of plants, fungi, amoebae, and animals. The domain name is an acronym of the first three genes in which it was identified: Lin-11 from The LIM domain is 50–65 amino acids in length and is defined by two cysteine-histidine-rich zinc fingers separated by a hydrophobic linker. The defining feature of the domain is its eight structural zinc-coordinating residues . Outside of these highly conserved residues, LIM domains are highly diverse and lack a consensus protein-binding sequence , Salpingoeca rosetta (Choanoflagellatea), Monosiga brevicollis (Choanoflagellatea), Amphimedon queenslandica (Porifera), Mnemiopsis leidyi (Ctenophora), Nematostella vectensis (Cnidaria), Trichoplax adhaerens (Placozoa), Drosophila melanogaster (Arthropoda), and Homo sapiens (Vertebrata); see We used the LIM hidden Markov model (HMM) from PFAM Given this poor statistical support, we used a consensus approach to identify consistently recovered clades. We generated a strict consensus tree between a pruned version of the multi-species tree and the human-only dataset. We designated each of the 38 clades radiating from the midpoint of this strict consensus tree as human LIM homology groups. Out of 171 human LIM sequences, only 12 were placed in homology groups with three or fewer taxa. Superimposing these homology groups onto the multispecies tree in We retrieved the full amino acid sequences of all 265 hypothetical proteins and scanned them for non-LIM PFAM domains using HMMER Drosophila ABLIM protein unc-115 lead to axon navigation errors ABLIM genes code for focal adhesion and adherens junction scaffolding proteins that mediate interactions between actin filaments and cytoplasmic targets; they also activate cytoskeletal signaling cascades that lead to transcription Drosophila, Nematostella, and Amiphimedon with the canonical architecture of four LIM domains and a VHP domain  as “unclassified” rather than a bona fide CRP, since we are unable to generate any corroborating evidence to ally this protein with the CRP class.If we root our multi-species tree with CRP, which is reasonable given that CRP is present in plants, the LIM domains of this class form a clade that is almost monophyletic  and S3. Nematostella, one in Mnemiopsis, two in Amphimedon, and two in Capspaspora  motif helps localize the Pdlim7 protein to α-actinin ein Ldb3 . This suNematostella and Mnemiopsis Enigma proteins , as well as a Tungus protein encoded by the cephalochordate Branchiostoma floridae (Bf_123730). This suggests that this motif was also established prior to the divergence of these three families.A second motif of unknown function, the Alp motif (AM), was previously thought to be present only in the Alp family of proteins  Drosophila, a single ENIGMA class protein, Tungus, exists with a PDZ domain and four LIM domains. The first Tungus LIM forms a clade with the LIM domain from the Alp family, while the other three LIM domains are related to each of the three Enigma LIM domains  and the invertebrate chordate Branchiostoma floridae (Bf_123730), but absent from all other species in our study . We were unable to recover an Alp from any of the non-bilaterian species, Drosphila, or C. elegans, but we did find Alp proteins in Capitella (JGI Capca1|190169) and Branchiostoma (Bf_124330), as well as human.We found a single Enigma protein in Capitella, as well as in non-bilaterian genomes; all these data were unavailable at the time of the previous study. The presence of the ALP motif throughout the ENIGMA class further contradicts this hypothesis. The most parsimonious explanation given this new data is that an Enigma-like ancestor originated in the stem of the Metazoa and gave rise to the Alp and Tungus families in the stem of the Bilateria  with the cytoskeleton Drosophila, Trichoplax, Nematostella, Amphimedon, Salpingoeca, Capsaspora, as well as three in Monosiga  are most highly expressed in the brain as an actin cross-linking structural protein  is identical to the length of the corresponding interval between the LIM domain and the single nebulin repeat in the Branchiostoma floridaeOutside of the LASP class, we were unable to find other nebulin repeat-containing proteins in any of the non-human species in this study. This is consistent with previous studies that report only being able to find nebulin repeat-containing proteins in vertebrates and the cephalochordate LIM homeodomain proteins (LHX) are transcription factors that usually consist of two amino-terminal LIM domains and one carboxyl-terminal homeodomain . This clDrosophila, LHX proteins are involved in axon guidance, patterning, and muscle formation  proteins are co-expressed with LHX proteins and are thought to play a role in antagonizing selected LHX combinations  from the LIMK class. These two have atypical domain architectures, which lack PDZ domains; in addition, each contains more than two LIM domains, none of which share phylogenetic affinity with the bona fide LIMK LIM domains. The other (Co_05847T0) has a typical LIMK domain architecture, but also contains an additional TFIIA domain (Pfam PF03153). Although the first LIM of this protein is highly divergent, the second LIM is phylogenetically related to the second LIM of the metazoan LIMK proteins , had previously been designated as an LMO7 Nematostella, we recovered a single protein (Nv_216756) with a LIM domain and a degraded CH, but no PDZ. Interestingly, we identified LMO7 proteins, each with a single PDZ and CH domain, in Amphimedon and Mnemiopsis, but did not find any LMO7 proteins in the non-metazoan species. The presence of these proteins in the two earliest animal lineages suggests that LMO7 originated at the stem of the Metazoa  domain and a highly conserved carboxyl-terminal region, represented by PFAM model DUF3585  is a class of focal adhesion scaffolding and integrin-mediated signaling proteins When phosphorylated, PXNs can recruit complexes of proteins to focal adhesions and regulate Rho GTPase signaling to effect cell adhesion, spreading, motility, and survival  proteins are adapters responsible for focal adhesion assembly and linking integrins to multiple signaling pathways  pathway. In this pathway, these proteins antagonize Dsh on the proximal side of the cell, inducing a distal Fz-Dsh complex and establishing cell polarity  family . We recotostella . There i Metazoa .Nematostella and Drosophila Etes family proteins. Humans lack an Etes family protein and are the only species in our study with Fhl proteins. The most parsimonious explanation for this data is that an ancestral Etes-like protein lost its PET domain somewhere in the lineage to humans after it split from Drosophila  proteins contain four LIM domains and a LIM-like amino-terminal zinc-finger domain  family are highly expressed in striated muscle, osteoblasts, and testes, where they have documented interactions with more than 50 other proteins ZYX (Zyxin) class proteins act as adapter proteins that facilitate the assembly of protein complexes at focal adhesions and take part in traffic to and from the nucleus  conservation of LIM quantity, (2) phylogenetic affinity of LIM domains with the LIM domains of human proteins within the class, (3) presence of non-LIM domains and/or motifs that are characteristic of the group, and (4) correct order of LIM and non-LIM domains and/or motifs.Drosophila, Trichoplax, and Amphimedon  with the conserved architecture of an amino-terminal LIM domain and a carboxy-terminal RasGTP association domain (Pfam PF00788). Further phylogenetic analysis is needed to assess whether this group represents a novel class of metazoan LIM proteins.Most of these 59 proteins include domain architectures not seen in any of the described classes. Many of these proteins could not be categorized since they represent lineage-specific innovations that no longer fit the criteria for membership to an existing class. Others may be the result of erroneous gene predictions in the genomic region of a classifiable LIM gene. However, we were able to identify a group of possibly related proteins from It is worth noting that 37 of the 59 unclassified LIM proteins are from the three non-metazoan species. This is not surprising, since the non-metazoan species have had a longer stretch of independent evolution and have experienced much different selective pressures than metazoans, especially in terms of their cell surface environments.We also note here that this study did not characterize two of the 73 described human LIM genes, SCEL and LIMS3L. These genes have been included in the “Unclassified” section of LIM domain-containing proteins have a range of binding partners and are considered “molecular adapters” because of their ability to assemble proteins that would otherwise be unable to interact directly. The binding flexibility of the LIM domain is also used for autoregulation, as well as for the combinatorial or direct regulation of other proteins. Most LIM proteins serve in cytoskeletal complexes but can also translocate to the nucleus to regulate transcription. In this way, they are vital for communicating extracellular signals between the surface of a cell and the nucleus. This dual localization makes LIM proteins important for the modulation of cell motility, structure, and division.In this study, we have identified 265 LIM domain-containing proteins from nine proteomes. We divided this LIM complement up into 14 classes. Our classification relied on both phylogenetic analyses of LIM domains, as well as domain and motif architecture; in one case, phylogenetic analyses of non-LIM domains were also applied. For each class and family, we have provided plausible estimates of origin, which are summarized in Novel combinations of protein domains have been produced by domain fusion and recombination events throughout evolution. These events (and their fixation) are somewhat rare, but have been shown to be relatively constant, with bursts of increased domain promiscuity occasionally occurring between various ancestral nodes Trichoplax appears to have lost the LASP, LMO7, LIMK, ZYX and CRP classes. If it is true, as most phylogenetic  will continue to further our understanding of the history of the LIM superclass, allowing for a more precise chronicle of the evolution of the individual LIM classes and families. Furthermore, because human LIM proteins are implicated in diseases as diverse as leukemia, epilepsy, cardiomyopathy, osteoporosis, and muscular dystrophy, understanding the evolutionary history of this superclass can help translational researchers with the identification of medically relevant sequence motifs, the determination of appropriate model species, and the proper association of findings from model systems to human homologs Nematostella v. 1.0 Trichoplax v. 1.0 Monosiga v. 1.0 Amphimedon predicted proteome was downloaded from the link provided in the genome paper (ftp://ftp.jgi-psf.org/pub/JGI_data/Amphimedon_queenslandica/assembly/) Capsaspora and Salpingoeca were downloaded from the Origins of Multicellularity Sequencing Project, Broad Institute of Harvard and MIT (see http://www.broadinstitute.org) in March 2011. The Drosophila v. 3.0 proteome was downloaded from the FlyBase Web site Mnemiopsis sequencing effort, we generated protein-coding gene models using a combination of Fgenesh Mnemiopsis proteins used in this study are publicly available in GenBank. GenBank accession numbers for all Mnemiopsis sequences used in this study can be found in The filtered protein models for Drosophila sequences the rest of the name is the Entrez gene symbol or the FlyBase name, respectively . In the case of human sequences with more than one isoform, the Entrez gene symbol is followed by a hyphen and the number or letter of the isoform as it appears in RefSeq. In the case of genomes sequenced by the Joint Genome Institute, the JGI ID follows the underscore . For Amphimedon sequences, we used the first number in the sequence header . For Salpingoeca and Capsaspora, we use the complete gene model ID that was assigned by the Origins of Multicellularity Sequencing project. Similarly, we used the Mnemiopsis gene model IDs that our group generated as part of the Mnemoipsis genome project. We refer to the LIM domains within these sequences in amino to carboxyl order .For convenience, we have adopted a simplified naming convention to refer to sequences. For all sequences, the first two characters refer to the genus and species followed by an underscore. For human and We used the LIM HMM (Pfam PF00412.15) from the Pfam protein domain database Drosophila genes with alternatively spliced transcripts, we selected a single representative isoform. We discarded proteins with domains that were highly truncated or had very poor sequence conservation. These sequences represented zinc fingers that were mispredicted as LIM domains. In one case, (Ta_20314) a zinc finger made it into our data set and trees, but was later removed after we performed more detailed analyses. For each domain sequence in our main dataset, all characters predicted as insertions within the HMM (represented as lowercase letters) were removed. We added all individually processed domains to a single file to construct our nine-species alignment (For human and lignment .raxmlHPC-MPI -f d -m PROTGAMMAILG -s input.phy -#25 -d –k), and another with 25 parsimony starting trees (raxmlHPC-MPI -f d -m PROTGAMMAILG -s input.phy -#25 -k).We used maximum likelihood (ML) and Bayesian methods in a likelihood framework to construct two phylogenetic trees. We generated one tree  and S3 fprset aamodelpr = fixed(wag); lset rates = Invgamma; mcmp mcmcdiagn = no nruns = 1 ngen = 5000000 printfreq = 5000 samplefreq = 500 nchains = 5 savebrlens = yes; mcmc;). All runs were found to be asymptotic before the relative burn-in fraction of 0.25. We computed likelihood scores for all trees using the LG matrix in PHYML v3.0 phyml -i 01-Input.phy -c 4 -m LG -a e -o lr -f d -u 01-Input.tre -v e -d aa -b 0 -s NNI). We then chose the tree with the highest likelihood from all 50 ML searches and both Bayesian trees (raxmlHPC-MPI -m PROTGAMMAILG -s 01-Input.phy -N 100 -n 100BS –k).We used MrBayes v. 3.1.2 to construct Bayesian trees for each dataset an trees  and S3. Because bootstrap support for the main dataset phylogeny was poor, we used a consensus approach to identify clades that were recovered independently in both the main dataset and the human-specific subset. We created a strict consensus cladogram of human taxa using Beginning with the nine-species tree , we usedE-value above 0.05 were excluded. In the case of overlapping domain envelopes, the prediction with the lowest independent E-value was selected. Predictions removed in this manner were checked individually.We used the HMMER program hmmscan and Pfam v 24.0 to detect other domains in all the proteins of our main dataset E-value below 0.05. The TANTAN program was run using its default settings. We then ran the MEME motif discovery program iteratively, searching for a single motif in at least four proteins with the following command line (meme -minsites 4 -p 6 -maxsize 1000000 INPUT_FILE) E-values greater than 0.01 were reported. The results of these analyses are shown in Low complexity regions were masked out of all proteins in the main dataset using TANTAN v. 3 We ran MEME on an unmasked version of the LIM proteins to identify instances of existing motifs that may have been masked. We did not consider new motifs from this unmasked alignment, but in some cases extended existing motifs. All modifications stemming from this unmasked analysis are indicated in We identified multiple motifs in the highly conserved N-terminus in MICAL proteins in the motif discovery analysis. We aligned the proteins containing these motifs using MUSCLE v3.8.31 Caenorhabditis elegans WS219 (from Wormbase), Capitella teleta v1.0 (from JGI), Lottia gigantea v1.0 (from JGI), Saccoglossus kowalevskii (from RefSeq), Strongylocentrotus purpuratus (from SpBase), Branchiostoma floridae v2.0 (from JGI), Ciona intestinalis v2.0 (from JGI), Gallus gallus (from Refseq), Danio rerio (from Refseq) C. elegans, Capitella, and Branchiostoma genomes to ensure that no unpredicted genes were omitted from these species  to identify proteins containing both PDZ and LIM domains in each additional species et al.Brianchiostoma, Capitella and C. elegans PDZ-LIM models identified above with the following command (hmmsearch –max –incE 10 AM_MOTIF.hmm Input.fa). The results are reported in We searched for the Zasp Motif in all proteins in the main dataset using the corresponding SMART HMM SM00735;  [40]. ThIn order to increase our confidence that nebulin repeats are specific to the Lasp family in non-bilaterians, we performed the following analysis. First, we ran Augustus and HMMgene on each of the non-bilaterian genomes in our study We classified the human LIM proteins into 14 classes based on sequence similarity and domain architectures. Our phylogenetic analysis validates these groups. We assigned non-human LIM proteins to these groups if they (1) shared the same number of LIM domains as human members of the class, (2) shared the same complement of LIM homology groups as human members of the class; (3) shared the conserved order of LIM domains found in human members of the class, and (4) shared non-LIM domains, motifs, and arrangement of these architectural features distinctive of the class.de novo in the Amphimedon and Salpingoeca genomes and created a multiple alignment of the LIM domains found in these models. To this alignment, we added LIM domains found in JGI unfiltered protein models for Nematostella, Trichoplax, and Monosiga. After removing duplicates from our main analysis, we repeated the full phylogenetic and LIM domain classification analyses to place these LIM domains into homology groups. For each species, we looked for homology groups not present for that species in the main dataset. We recovered one Amphimedon protein in this analysis and submitted it to Genbank (GenBank JN615191).To be certain that species-specific class absences of classes were not a result of errors in published proteomes, we performed the following analysis. First, we used Fgenesh For some JGI proteins, we found alternative models with more conserved domain architectures than the filtered model following phylogenetic characterization of the LIM domains. When a superior model was discovered, that model (and not the filtered model) was entered into Figure S1Multiple sequence alignment of LIM domain. This alignment includes LIM domains from nine species. The alignment is in FASTA format. Due to the automatic nature of our LIM identification, many of the LIM domains are incomplete, especially at the carboxyl-terminus. This is discussed in more detail in the (FA)Click here for additional data file.Figure S2LIM domain tree. Midpoint rooted phylogram of LIM domain phylogeny (maximum likelihood). Alternating blue and grey coloring delineates homology groups; black regions are unclassified. Conserved LIM group labels appear within the upper edge of a clade. See (PDF)Click here for additional data file.Figure S3LIM domain tree in Newick format. Newick version of (TRE)Click here for additional data file.Figure S4Human LIM domain tree. Midpoint rooted phylogram of human LIM domain phylogeny (maximum likelihood). See (PDF)Click here for additional data file.Figure S5Human LIM domain tree in Newick format. Newick version of (TRE)Click here for additional data file.Figure S6Multiple sequence alignment of ENIGMA, LIMK, and LMO7 LIM domains. This alignment contains the subset of sequences from Branchiostoma floridae, Caenorhabditis elegans, Capitella teleta, Ciona intestinalis, Danio rerio, Gallus gallus, Lottia gigantea, Saccoglossus kowalevskii, and Strongylocentrotus purpuratus were added to this alignment. The alignment is in FASTA format.(FA)Click here for additional data file.Figure S7LIM domain tree from ENIGMA, LIMK, and LMO7 class proteins. Midpoint rooted phylogram of ENIGMA, LIMK, and LMO7 class LIM domain phylogeny (maximum likelihood). See (PDF)Click here for additional data file.Figure S8LIM domain tree in Newick format from ENIGMA, LIMK, and LMO7 class proteins. Newick version of (TRE)Click here for additional data file.Figure S9Hidden Markov models for conserved MICAL amino-terminus region. This RAR file contains two HMMs that span from the MICAL amino-terminus to the CH domain. One is amino-terminal to the FAD_Binding3 Pfam domain; the other is carboxyl-terminal. The files are in HMMER format.(RAR)Click here for additional data file.Table S1Classification of LIM proteins. Species, accession numbers, and domain architectures are provided for each LIM protein in our analysis. Blue and grey columns indicate the amino acid position of a particular domain or motif as well as the E-Value from hmmsearch, in the case of domains, and MEME, in the case of motifs. Blank blue and grey columns indicate that the particular domain or motif was not found. A single asterisk indicates a feature that was not identified in the original protein sequence, but is present in alternative protein models. A note at the end of the row describes the alternative model associated with the asterisk. A double asterisk refers to a class-level note listed at the top of the class. Domains in red indicate domains that are not typical of the class.(XLS)Click here for additional data file.Table S2Motifs of LIM proteins. Each motif includes a MEME score in parenthesis next to the motif name, as well as a regular expression that defines the motif. We manually adjusted regular expressions in some cases to ensure that they matched all sequences identified by MEME. Residues in red represent those that were discovered by MEME using an unmasked version of the LIM proteins. Notes at the bottom of a section indicate other proteins where this motif was identified in the unmasked version of the MEME analysis. In the case of motifs missed by MEME, but discovered using our manually adjusted regular expression, the term “Regex” appears in the E-Value column.(XLS)Click here for additional data file.Table S3LIM domain homology groups. We created 38 LIM domain homology groups based on concordant clades from a strict consensus of our human LIM domain tree (ain tree  and a prain tree . We assi(XLS)Click here for additional data file.


Genome size and complexity, as measured by the number of genes or protein domains, is remarkably similar in most extant eukaryotes and generally exhibits no correlation with their morphological complexity. Underlying trends in the evolution of the functional content and capabilities of different eukaryotic genomes might be hidden by simultaneous gains and losses of genes.We reconstructed the domain repertoires of putative ancestral species at major divergence points, including the last eukaryotic common ancestor (LECA). We show that, surprisingly, during eukaryotic evolution domain losses in general outnumber domain gains. Only at the base of the animal and the vertebrate sub-trees do domain gains outnumber domain losses. The observed gain/loss balance has a distinct functional bias, most strikingly seen during animal evolution, where most of the gains represent domains involved in regulation and most of the losses represent domains with metabolic functions. This trend is so consistent that clustering of genomes according to their functional profiles results in an organization similar to the tree of life. Furthermore, our results indicate that metabolic functions lost during animal evolution are likely being replaced by the metabolic capabilities of symbiotic organisms such as gut microbes.While protein domain gains and losses are common throughout eukaryote evolution, losses oftentimes outweigh gains and lead to significant differences in functional profiles. Results presented here provide additional arguments for a complex last eukaryotic common ancestor, but also show a general trend of losses in metabolic capabilities and gain in regulatory complexity during the rise of animals. Trichoplax adhaerens with no internal organs and only four different cell types [Caenorhabditis elegans possesses about 19,000 protein coding genes [Drosophila melanogaster has a genome of only about 14,000 genes [Eukaryotic organisms exhibit an enormous diversity on many different levels . Besidesll types  to mammall types ,4. Yet, ll types , whereasng genes , and the00 genes . In orde00 genes , numerou00 genes ,9), alte00 genes , transpo00 genes , detaile00 genes , the com00 genes ,14, and 00 genes ,16.While biologists have long been enthralled by the vast diversity found amongst modern eukaryotes, the underlying evolutionary history that led to this vast diversity is at least equally fascinating and is likely to help our understanding of extant organisms and their molecular biology. An intuitive view of eukaryote evolution is that the last eukaryotic common ancestor (LECA) was 'simple' and that accretion of features over time led to complex, multicellular organisms, such as plants and animals. Recently, an increasing number of studies are surfacing that suggest that many aspects of the LECA might not have been 'simple' and that it probably already had many features commonly associated with modern eukaryotes . For exaA succinct way to describe the functional potential of large groups of genes, such as complete genomes or metagenomes, is to list and analyze the set of recognized domains present in proteins encoded by the genes in a given group. Recently, a term 'domainome' was proposed for such sets . ProteinIn this work, we investigate the evolution of the domain repertoires of eukaryotic genomes. To gain a more complete picture of this evolution, we reconstruct the domainomes of ancestral species at important branching points of the eukaryotic tree of life, such as the LECA and the Urbilateria . While parts of putative genomes for relatively recent ancestral species have been reconstructed successfully , 3 from amoebozoa, 17 from archaeplastida, 16 from chromalveolata, and 5 from excavate, thus covering 5 of the 6 eukaryotic 'supergroups' ,31 (we wWhile the number of distinct domains found in extant species shows a weakly growing trend (with outliers) with the apparent morphological complexity Figure , compariCapitella teleta and the leech Helobdella robusta) and one mollusk (the snail Lottia gigantea) is larger than that of ecdysozoans, and the numbers of domains gained and lost relative to the Urbilateria are smaller  [N. vectensis, as its number of domains  is comparable to that of lophotrochozoans and surpasses all ecdysozoans analyzed here. This unexpected 'genomic' complexity  of N. vectensis (and likely other cnidarians as well) has also been noted on the level of regulatory networks  , especiaanemone) , compareanemone) . This momple, in ,46). ThiNematostella and other outliers  and Metamonada  from the Excavata group [Plasmodium falciparum) from the Alveolata group (shown in brown) are the most derived relative to the LECA. On the other hand, this graph differs from the eukaryotic evolutionary tree in that some groups that are closely related appear quite distant, most strikingly seen in the large separation between fungi and animals, with fungi having the highest percentage in catalytic activity and animals having among the lowest. It is also noteworthy how similar all vertebrate genomes are to each other on this level, despite roughly 400 million years since the separation between ray-finned fish and tetrapods [Figure ta group  , at this point covering around 60% of most eukaryotic genomes, we expect the number of domain gains to grow with more complete versions of Pfam. However, we don't expect this would reverse our findings presented here. To test this, we compared the analysis presented here, which uses the current version of Pfam (24.0) with over 10,000 domain models, with results obtained with previous versions of Pfam. While the overall number of domains significantly increases with each release of Pfam, often by >20% with each release, overall tendencies are independent of the Pfam version used . Interestingly, while the domain repertoire of P. tetraurelia is small, its gene number of around 40,000 is very high. It has been shown that the genome of P. tetraurelia is the result of at least three successive whole-genome duplications [T. thermophila also has a high gene count, around 27,000, yet this seems to be due to numerous small duplication events, as opposed to whole genome duplications [T. thermophila shares more orthologous genes with humans than are shared between humans and the yeast Saccharomyces cerevisiae [The domain repertoires of the ciliates ications , explainications . It has revisiae , despiteHorizontal gene transfer clearly has the potential to result in misleadingly inflated domain counts of ancestral species. Despite being more common in eukaryotes than previously thought, most known cases of horizontal gene transfer in eukaryotes involve bacteria as donors -55. To aClearly, domain content of ancestral genomes and the overall pattern of domain gains and losses are dependent on the details of the eukaryotic evolutionary tree used for the Dollo parsimony based reconstruction. There is an ongoing controversy concerning the details of the phylogenetic tree of eukaryotes . In theGiardia lamblia and Trichomonas vaginalis, both human parasites, at the base of the tree. Clearly these two parasites are highly derived and unlikely to exhibit much resemblance to the LECA [Naegleria gruberi, the domain count of the ancestral eukaryote  approaches the mean for extant nematodes . On the other hand, while the topology of the eukaryote tree of life used influences domain counts close to the root, it has no significant effect on the results concerning the functional dynamics of eukaryote genomes during evolution.As expected, results based on the 'crown group' eukaryote tree appear to lead to strongly different domain counts for the LECA . However, this result is based primarily on a clade of Metamonda, namely the LECA . Moving Emiliania huxleyi, the placement of which on the tree of life has no measurable effect on the results presented here (data not shown).Finally, we would like to point out that the model shown in Figures Clearly, studies such as the one presented here will be more accurate and informative once more eukaryote genomes have been released covering the tree of life more uniformly, since there is currently still a bias towards commercially important species as well as traditional model organisms. For example, for animals, an increased coverage of lophotrochozoans would be desirable. Improved sampling over species space is also expected to go hand in hand with increased coverage of domain space by Pfam and similar databases.In this work we show that domain losses during eukaryote evolution are numerous and oftentimes outnumber domain gains. This, combined with estimates for large numbers of domains present in ancestral genomes, is an additional argument for a complex LECA. The functional profiles of gained and lost domains are very different; for instance, during animal evolution gained domains involved in regulatory functions are enriched, whereas lost domains are preferentially involved in metabolic functions, especially carbohydrate and amino acid metabolism. This makes it seem likely that animals over time outsourced a portion of their metabolic needs. Clustering inferred ancestral domainomes according to their functional profiles results in graphs remarkably similar to the eukaryotic tree of life.Protein predictions for 114 completely sequenced eukaryotic genomes were obtained from a variety of sources; for details, as well as information regarding numbers of protein predictions, see Additional file The domain repertoire for each genome was determined by hmmscan  from the HMMER 3.0b2 package  using hiBased on these preprocessing steps, a list of domains was created for each of the 114 genomes and, together with each of the three eukaryotic evolutionary trees described in the text, used for a Dollo parsimony  based in-4 to 10-18, as well as domain specific 'noise' (NC) and 'trusted' (TC) cutoff values from Pfam, with or without overlap and/or viral domain removal. We were unable to find a combination of these settings that would significantly change the numbers presented here and invalidate our conclusions. For example, Additional file -8.In order to assess the robustness of our results relative to preprocessing steps, we also performed our analyses with a variety of different parameter combinations, such as uniform E-value based cutoffs ranging from 10Pfam domains  where mapped to GO terms by using the 'pfam2go' mapping (dated 2009/10/01) provided by the GO consortium . GO termThe preprocessing steps, the Dollo parsimony approach, and basic ancestral GO term analyses, were performed by software of our own design .GO: gene ontology; LECA: last eukaryotic common ancestor.CMZ performed the analysis; CMZ and AG contributed to the research design and discussion on the manuscript; CMZ and AG wrote the manuscript. Both authors read and approved the final manuscript.Table of genomes analyzed.Click here for fileTable of Pfam domain counts in extant species. Summary of conditions used: protein predictions as listed in Additional file Click here for fileDomain gains and loss counts during eukaryote evolution. Inferred domainome sizes are shown in blue, domain gain counts in green, and domain loss counts in red. Numbers in brackets are average domainome sizes of all extant descendents of each node. Summary of conditions used: protein predictions as listed in Additional file Click here for fileDomain gains and losses during eukaryote evolution. phyloXML [phyloXML  formattephyloXML . SummaryClick here for fileDomain gains and corresponding GO terms during eukaryote evolution. Summary of conditions used: protein predictions as listed in Additional file Click here for fileDomain losses and corresponding GO terms during eukaryote evolution. Summary of conditions used: protein predictions as listed in Additional file Click here for fileDomain gain and loss counts during eukaryote evolution under a coelomata model. Summary of conditions used: protein predictions as listed in Additional file Click here for fileDomain gains and loss counts during eukaryote evolution under a 'crown group' model. Summary of conditions used: protein predictions as listed in Additional file Click here for fileTable of enriched gained and lost GO terms evolution under a coelomata model. The two terms with the lowest P-values are shown. Summary of conditions used: protein predictions as listed in Additional file Click here for fileTable of enriched gained and lost GO terms under a 'crown group' model. The two terms with the lowest P-values are shown. Summary of conditions used: protein predictions as listed in Additional file Click here for fileFunctional analysis of the human domainome complemented with intestinal bacteria. Summary of conditions used: protein predictions as listed in Additional file Click here for fileDomain counts for a variety of cutoff values.Click here for fileDomain gains and losses during eukaryote evolution for a E-value cutoff of 10-8. Summary of conditions used: protein predictions as listed in Additional file Click here for fileComparison of enriched gained and lost GO terms along path from Unikonta to Mammalia using different calculation methods and different approaches for multiple testing correction. The two terms with the lowest P-values are shown , with tClick here for file