Caenorhabditis elegans and Drosophila genomes can be queried on our new web server, PhyloGene. PhyloGene provides intuitive and user-friendly platform to query the patterns of conservation across 86 animal, fungal, plant and protist genomes. A protein query can be submitted either by selecting the name from whole-genome protein sets of the intensively studied species or by entering a protein sequence. The graphic output shows the profile of sequence conservation for the query and the most similar phylogenetic profiles for the proteins in the genome of choice. The user can also download this output in numerical form.Proteins that function in the same pathways, protein complexes or the same environmental conditions can show similar patterns of sequence conservation across phylogenetic clades. In species that no longer require a specific protein complex or pathway, these proteins, as a group, tend to be lost or diverge. Analysis of the similarity in patterns of sequence conservation across a large set of eukaryotes can predict functional associations between different proteins, identify new pathway members and reveal the function of previously uncharacterized proteins. We used normalized phylogenetic profiling to predict protein function and identify new pathway members and disease genes. The phylogenetic profiles of tens of thousands conserved proteins in the human, mouse,  Phylogenetic profiling of a protein specifies the relative sequence conservation or divergence among orthologous proteins across a set of reference genomes. Proteins that function together, for example members of the same pathway or protein complex, often show similar pattern of conservation across phylogenetic clades. As an extreme case, when a species is no longer under evolutionary pressure to maintain a protein complex, pathway, organelle or specific function, the corresponding proteins are lost or show a strong divergence. Phylogenetic profiling based on the binary calls of the presence or absence of orthologs in the surveyed genomes produced impressive results in evolutionary analyses of a wide variety of prokaryotes and of eukaryotic organelles, such as the cilia ,2 and miRecently, we developed Normalized Phylogenetic Profile (NPP) analysis based on a continuous measure of sequence similarity that is adjusted to evolutionary distance between species. The level of protein conservation for the ortholog in a given genome is normalized to the scale of conservation for all proteins in this genome, in effect taking into account the genome-wide distribution of conservation values expected for this individual species. This method captures protein sequence divergence and partial loss in the context of inter-species phylogenetic distances. Starting with query proteins from a few well-studied genomes, we surveyed and compared their phylogenetic profiles across full protein sets from 86 eukaryotic genomes. These analyses revealed novel components in various biological pathways including RNAi, m6A RNA methylation and the MITF pathway, as well as identified novel proteins associated with melanoma and other diseases –11.Here we describe the implementation of the NPP algorithm on a web-based server, PhyloGene, which allows the user to submit protein queries, inspect the output in an interactive graphic format and download the output in numerical format.r are calculated between Z-score profiles for proteins of the query genome, and the top most similar phylogenetic profiles are recorded for each query protein. To estimate statistical significance of the resulting correlation, we use a naive null model of randomly shuffled species (columns of the table). For each pair of the compared profiles, we perform N = 1000 instances of random shuffling of values in one of the profiles and generate the corresponding random Pearson correlation coefficients (r). Based on the distribution of these random coefficients, we calculate a measure of statistical significance as the Z-score for the actual coefficient r.To generate phylogenetic profiles, NPP –11 perfoThe pre-computed top profiles along with the corresponding Pearson correlation coefficients and estimates of their statistical significance are displayed when the query protein is selected from the standard protein list. As a general rule of thumb, we observe that marginal Pearson correlation coefficient of 0.5 typically corresponds to a Z-score of ∼5.0, whereas highly significant Pearson correlation coefficient of 0.95 typically corresponds to a Z-score of ∼8.0. These Z-score values can be used as approximate cutoffs for insignificant and extremely significant similarities, respectively. It is important to mention that the Z-scores can be used only as rough estimation of the biological significance. A complimentary approach should take into account the biological knowledge about the query gene and estimate the significance of its co-evolved genes by the number of genes in the list that are known to interact with it. For example by integrating the PhyloGene results with other data sources like protein-protein interaction maps, results from high throughput screening or validated genetic pathways and find overlap between the lists as we previously showed .When an individual query protein is submitted as a sequence, the workflow is modified: (i) run BLASTP with the submitted sequence as a query against protein databases of multiple eukaryotic genomes and choose the top BLAST hit for each genome; (ii) filter out low scores (BLAST similarity score <50); (iii) normalize BLASTP bit scores by the highest score among genome hits; (iv) transform top bit score against each ‘subject’ genome into a Z-score based on pre-computed mean and standard deviation of scores generated by BLAST searches with all proteins from the selected query genome against this ‘subject’ genome. Finally, Pearson correlation coefficients are calculated between the query Z-score profile and the pre-computed Z-score profiles for proteins from the genome of interest, followed by the selection of the top phylogenetic profiles with the highest similarity to that of the query protein. Pearson correlation coefficients for each of the top profiles and their statistical significance are calculated and displayed as described above.PhyloGene is an intuitive and easy to use web tool that implements both of these modes. At the front page, the user can submit the query in the left panel and view the graphic output as a heatmap of sequence similarity patterns across multiple genomes in the right panel. The numerical output for more detailed analysis can be downloaded using a link to the Excel table in the left pane. The query can be submitted by (i) selecting a protein name from the menu of all proteins for a genome of the user's choice or (ii) submitting a protein sequence by pasting in a window or uploading the sequence file. In the left pane, the user can select the query genome of interest and the number of top most similar phylogenetic profiles to display. The front page also includes the link to a brief tutorial on using the tool.Homo sapiens, Mus musculus, Drosophila melanogaster or Caenorhabditis elegans); (ii) query protein, by either selecting the gene of interest from the list or entering a protein sequence and (iii) the number of most similar phylogenetic profiles to display .The user can submit the query in the left pane of the front page by setting (i) organism of interest  website with the information about this gene.The y-axis of the heatmap corresponds to the proteins from the query genome whose phylogenetic profiles are most similar to the profile of the query protein. These profiles are shown as rows of the heatmap; the number of displayed profiles is selected by the user during submission process. The top row corresponds to the profile of the query protein. Protein names are displayed at the left edge of the heatmap. Hovering over a protein name with the mouse brings up a short description of the gene; clicking on a gene name opens the link to the Ensembl  to dark blue (very high sequence similarity). Hovering over a square brings up the protein name corresponding to this row, the genome in which the homology search was performed, and the sequence similarity value ranging from 0 to 1; this is also the value that the color of the square is based on.r and Z-score.The first two columns of the heatmap are colored in yellow–dark red hue and represent Pearson correlation coefficient and statistical significance of the similarity to the query. Hovering over these columns brings up the values of In addition to visual inspection of the heatmap, the user can download the output in a numerical format as an excel table Figure .The tablr > 0.6, consistent with their known functional association with ALAD. Interestingly, many of the proteins with similar phylogenetic profiles have been implicated in distinct biological processes from heme biosynthesis, such as transaminase activity. For example SETD1A, SETD1B AADAT and SUV39H2 are implicated in lysine degradation; 16 other proteins with phylogenetic profiles similar to heme biosynthesis genes, such as FDXR, GPX1 and GOT2 are associated with the mitochondrion and several proteins are involved in lipid biosynthesis or processing, such as three ORM1-like proteins. These proteins regulate the biosynthesis of sphingolipids; and mutations in these proteins have been associated with the development of childhood asthma  as a query confirms previously known genes in this pathway and suggests new functional links of ALAD with other human proteins Figure . ALAD isd asthma . The simThe PhyloGene web server provides online access to our method of normalized phylogenetic profiling and has several immediate biological applications: (i) analyzing the conservation pattern of a given protein across eukaryotic genomes; (ii) predicting function of previously uncharacterized proteins, based on the phylogenetic profile similarity to proteins with known function; (iii) suggesting groups of proteins that co-evolved together and identifying new pathway members, based on inferred co-evolution with the known members of the pathway and (iv) revealing organismal adaptions to the environment on the level of proteins or pathways, which may provide clues to protein function.


Modern genomic techniques allow to associate several Mendelian human diseases to single residue variations in different proteins. Molecular mechanisms explaining the relationship among genotype and phenotype are still under debate. Change of protein stability upon variation appears to assume a particular relevance in annotating whether a single residue substitution can or cannot be associated to a given disease. Thermodynamic properties of human proteins and of their disease related variants are lacking. In the present work, we take advantage of the available three dimensional structure of human proteins for predicting the role of disease related variations on the perturbation of protein stability.We develop INPS3D, a new predictor based on protein structure for computing the effect of single residue variations on protein stability (ΔΔG), scoring at the state-of-the-art . We then filter 368 OMIM disease related proteins known with atomic resolution  with 4717 disease related single residue variations and 685 polymorphisms without clinical consequence. We find that the effect on protein stability of disease related variations is larger than the effect of polymorphisms: in particular, by setting to |1 kcal/mol| the threshold between perturbing and not perturbing variations of the protein stability, about 44 % of disease related variations and 20 % of polymorphisms are predicted with |ΔΔG| > 1 kcal/mol, respectively. A consistent fraction of OMIM disease related variations is however predicted to promote |ΔΔG| ≤ 1 kcal/mol and we focus here on detecting features that can be associated to the thermodynamic property of the protein variant. Our analysis reveals that some 47 % of disease related variations promoting |ΔΔG| ≤ 1 are located in solvent exposed sites of the protein structure. We also find that the increase of the fraction of variations that in proteins are predicted with |ΔΔG| ≤ 1 kcal/mol, partially relates with the increasing number of the protein interacting partners, corroborating the notion that disease related, non-perturbing variations are likely to impair protein-protein interaction . The set of OMIM surface accessible variations with |ΔΔG| ≤ 1 kcal/mol and located in interaction sites are 23 % of the total in 161 proteins. Among these, 43 proteins with some 327 disease causing variations are involved in signalling, structural biological processes, development and differentiation.We compute the effect of disease causing variations on protein stability with INPS3D, a new state-of-the-art tool for predicting the change in ΔΔG value associated to single residue substitution in protein structures.  The analysis indicates that OMIM disease related variations in proteins promote a much larger effect on protein stability than polymorphisms non-associated to diseases. Disease related variations with a slight effect on protein stability  frequently occur at the protein accessible surface suggesting that they are located in protein-protein interactions patches in putative human biological functional networks. The hypothesis is corroborated by proving that proteins with many disease related variations that slightly perturb protein stability are on average more connected in the human physical interactome (IntAct) than proteins with variations predicted with |ΔΔG| > 1 kcal/mol.  One of the key goals in the postgenomic era is the elucidation of the mechanisms at the basis of the relationship between genotype and phenotype. In particular, understanding how human genetic variations are associated to diseases is still an open problem and its solution is a crucial issue for exploiting the possibilities offered by the modern sequencing techniques in the framework of precision medicine , 2.The role of missense mutations inducing single residue variations (SRVs) in proteins has been widely investigated: several databases collect data about the relationship between SRVs and diseases  and seveBiophysical studies allowed to measure the thermodynamic effect that protein variations induce on protein stability . HoweverWe downloaded from the Humsavar dataset (release 2015_10 of 14 Oct 2015) a collection of 27,185 variations related to 3082 OMIM diseases, on 2367 different human proteins and retained only proteins endowed with a PDB structure (3D) covering at least 70 % of the protein sequence. The PDBSWS resource . To define the interaction interface of the complex, we collected the set of residues that are solvent accessible in the isolated chain and are buried in the complex.The solvent accessible surface area of residues in wild-type proteins has been evaluated with the DSSP program . In ordeInteracting partners of each protein were retrieved from the IntAct database  as downlINPS3D is a new tool for predicting the change of protein folding free energy induced by single residue variations. The performance of the structure based predictor along with that of the sequence based one  are showWe applied INPS (sequence based), INPS3D (structure based) and MAESTRO (structure based) to the OMIM variation set for estimating the change in protein folding free energy induced by the disease-related variations. For sake of comparison we also ran the tools on the POLY set, containing variations not related to diseases, on the same OMIM proteins. We used polymorphisms from the very same proteins that have also variations related to diseases, in order to constrain the ΔG value of the folded form and avoid possible biases due to the inclusion of other proteins. The results Fig.  confirm The results are similar with INPS; with Maestro, the fraction of disease-related variations predicted with low |ΔΔG| values increases to 74 % of the total. Our results, obtained with three independent predictors, corroborate the notion that protein stability perturbation  is associated to disease-related variations. However, at least half of the OMIM set is predicted to promote only a slight change in protein stability . The observation poses the question as to whether the thermodynamic property of the protein variant  can be linked to some structural/functional feature of the variation, specifically when it is disease causing. Many investigations addressed the issue of which structural features could be associated to disease related variations , on the propensity of the variation to be or not in an interaction patch, and finally on the relation of the protein variant to be in physical interaction with other proteins, considering ΔΔG values predicted with INPS3D.We analyse the distribution of the relative solvent accessibility (RSA) of the disease related mutations as a function of the free energy change predicted for the corresponding protein variant. Boxplots in Fig. In Fig. http://www.rcsb.org/pdb/download/download.do#Structures), in order to highlight the role of protein-protein interactions, when present, in the biological functional unit. As an additional feature, we also included the likelihood of each variations to be or not in an interaction patch . It appDistinguishing functional monomeric from multimeric biological assemblies highlights the relevance of the variations when they are located at the interface of protein complexes . In TablFrom the data reported in Tables Summing up, we show that disease related variations in proteins can promote a low |ΔΔG| value, particularly when they are located in accessible sites that are also interacting sites.As a follow up, one may consider to which extent protein variants with disease-related mutations located in solvent exposed sites and slightly perturbing the stability, are or not involved in interaction networks of physical interaction, as available in IntAct . We collThe proteins endowed with a large amount of non-perturbing and solvent exposed disease related variations seem to play a central role in the human protein-protein interaction network. Likely, a variation on the protein surface can affect the interaction affinity, affecting important biological pathways and leading to an altered phenotype, as recently described . Out of We address the problem of the perturbations of the protein stability by disease causing variations on a set of OMIM related proteins whose native structure is well solved. To this aim we implemented INPS3D, a tool for computationally estimating the change in ΔΔG value associated to single residue variations, taking as input protein structure. Our strategy is to adopt a predictor that scores at the state-of-the-art and we compare its performance to other state-of-the-art predictors. INPS3D exploits information extracted from protein structures and outperforms the recently released INPS, based only on sequence information. Moreover INPS3D outperforms state-of-the-art structure-based methods that perform similarly to INPS and well compares with MAESTRO, which recently became available as a web server . Both pr


Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding.Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes.Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs. Protein-protein binding is a known phenomenon in complex biological networks. The molecular principle of such binding is often elusive in nature. Understanding its driving forces using known protein complexes is essential. The analysis of existing protein-protein interaction (PPI) complexes from the Protein Data Bank (PDB)  is the kInterfaces are localized regions of surfaces with different physico-chemical properties compared to the rest of the surfaces, thereby driving binding to other molecules. Both physical and chemical features  govern the formation of protein interfaces as described elsewhere ,9,12-18.Interfaces were observed to be significantly polar as well as non-polar with few charged groups, similar to the characteristics of the protein surface . StructuiG), binding energy (BE), and electrostatics among these interface classes and their gleaned features are documented. We identified five key features  that are significantly different between the interface classes. These novel observations have implications for residue-level characterization of protein complexes to develop models for protein-protein binding prediction and docking studies.In this study, we have carried out a comprehensive structural analysis of 278 non-redundant heterodimeric protein complexes from the PDB. We estimated the relative difference in surface and interface polarities of each complex in the dataset, using percentage values of polar residues. This property divides the dataset into two interface classes as also observed in our previous study with a smaller dataset . Class AWe created a non-redundant heterodimer dataset of protein complexes from the PDB, using the RCSB PDB's advanced search interface. The following criteria were used for filtering: (i) resolution <= 3Å (ii) protein size >50 residues (iii) contains experimental data (iv) number of chains, entities and oligomeric state is set at 2 (v) devoid of DNA or RNA or a hybrid of such molecules with the protein or otherwise (vi) entries with mutations were not included and (vii) sequence identity cut-off is set to 30%, which is the minimum cut-off available in the PDB. As a second step, the USEARCH program  was used2 were considered for this analysis, as defined by Chakrabarti and Janin [The interface of PPI complex is calculated as the change in solvent accessible surface area (ΔASA) upon complex formation. The Surface Racer 5.0 program  was usednd Janin . ASA wasnd Janin .Interfaces are part of protein surface formed upon binding of individual subunits. Each protein complex has a specific composition of polar (P) and nonpolar (NP) residues at the surface (S) and at the interface (I). The distribution of polar and nonpolar residues at the interface of a protein complex describes the nature of the interface and the major driving force for binding. We have calculated the percentage of polar and nonpolar residues at the surface and interface for each complex in the dataset. Polar residues considered in the analysis are R, N, D, E, Q, H, K, S, T, and Y and non-polar residues are A, C, G, I, L, M, F, P, V, and W. Complexes were then grouped based on the relative difference in percentage of polar residues between surface (S) and the interface (I). Complexes with interface polarity less than the surface (represented as S>I) are grouped as class A, and those that have interface polarity greater than the surface (represented as S<I) are grouped as class B .We calculated the intermolecular hydrogen bonds for the dataset using HBPLUS program  at a disiG, in kcal/mol, with negative ΔiG values indicating hydrophobic interface) and for the heterodimer dataset. BE values were calculated using the DCOMPLEX program [PDBePISA webserver  was used program  with the program  to calcuwhere ū is the atom-atom potential of mean force between two atoms, i and j which are 'r' distance apart. The total is over atomic pairs which are not from the same residue and a K factor is used to avoid double-counting of residue-residue and atom-atom interactions . The binbind. Therefore, DCOMPLEX [where A and B are considered as two protein structures whose interface residues contribute most to ΔGDCOMPLEX  uses theThe surface electrostatic potential of chain A and chain B of a protein complex was calculated by solving Poisson-Boltzmann equation with dielectric constant (protein) of 4 using DEEPVIEW .The Wilcoxon signed-rank test , a non-pTable S1 shows the heterodimer dataset (278) divided into class A (165) and class B (113). Thus, 59.4% of complexes in our dataset belong to class A (relative surface polarity is greater than interface polarity), where non-polar interactions are predominant at the interface, as previously observed in a number of studies [Figure S1. Examples of class A and class B complexes representing predominant non-polar and predominant polar interfaces , to determine the type of interactions predominantly driving protein-protein binding. Additional File dataset 28 dividedclass B 1. Thus, 5 studies . Class Ae PDBsum  interactiG, BE) in R program (using Wilcoxon rank sum test), to determine whether structural features discriminate among class A and class B complexes. Interestingly, five structural features showed significant difference among the interface classes as shown in Figure We carried out a statistical analysis of all the structural features  measure is significantly different among the interface classes with p = 7.01E-30 Table .iG) is a measure of the interface stability in protein complexes [iG values are significantly different among interface classes with p = 7.43E-18 , as compared to class B complexes . The BE values are significantly different among interface classes with p = 2.63E-14 (Table The percentage of charged residues at the interface is estimated for both classes. The interface charged residues% is significantly different among interface classes with p = 6.58E-13 (Table The interface area (ΔASA) of a complex is an important structural characteristic of PPI. We observed that class A complexes demonstrate comparatively larger interfaces than class B complexes. The ΔASA is significantly different among the classes with p = 1.31E-08 Table .Figure S2), indicating that low quality of intermolecular hydrogen bonds is a characteristic of the large number of polar or charged residues across protein interfaces as previously observed [The stability of protein-protein binding depends on the number of hydrogen bonds and salt bridges formed between the two interacting subunits. Class A complexes show high correlation between intermolecular H-bonds and interface area (r = 0.9) as previously observed ,42. Howeobserved . AlthougFigure S3) as previously observed [iG values show relatively less correlation with interface area in class B complexes (r = -0.62) as compared to class A complexes . Moreover, the ΔiG and BE is highly correlated among the dataset (r = 0.88) and class A (r = 0.91), however shows limited correlation among class B .The BE values are proportional to interface area in the dataset , and binding energy) among the interface residue-level classes. This is a first attempt towards classifying the complexes based on interface residue-level classes for the characterization of PPI features amongst these classes. These observations corroborate the need for classification of complexes in determining their combinatorial features and drawing consensus for common patterns in protein-protein recognition. These results provide molecular insights for protein-protein binding towards the development of residue-level prediction models in future studies. Additionally, mutation experiments using hot spot residue databases [Structural analyses of known protein interfaces help in understanding the molecular principals of PPIs. Therefore, a comprehensive analysis of known structural interfaces of 278 complexes was carried out and their gleaned features are documented in this study. It is realized that each complex type is unique, specific and sensitive to binding. Nonetheless, there is a considerable degree of observed pattern among protein interface classes. We report two classes of interfaces, one class with less polar residues and the other class with more polar residues compared to the surfaces in bound state. The surfaces of proteins are quite polar and therefore, it is perhaps not surprising that some interfaces are polar as well and that PPI complex forms due to interactions among charged and polar residues. Thus, the need for a residue-level characterization of the interface is crucial in addition to other structural features. We document five discriminatory features , interface charged residues%, solvent free energy gain upon interface formation  divided into interface classes based on residue level relative surface-interface polarity. The PDB code is shown along with the specific chains used in this study.Click here for fileFigure S1: Class A and Class B are significantly different. The boxplot depicts class A and class B significantly different with a p-value of 1.66E-45 (using Wilcoxon rank sum test).Click here for fileFigure S2: Intermolecular H-bonds shows relatively low correlation with interface area in class B. Hydrogen bonds at the protein interface are highly correlated to interface area in the dataset (r = 0.88) and class A (r = 0.9), however shows relatively lower trends (r = 0.73) in class B.Click here for fileFigure S3: Binding energy is highly correlated to interface area. BEs at the protein interfaces are highly correlated to interface area with r = -0.96.Click here for fileiG) shows limited correlation with interface area in class B complexes. ΔiG shows high correlation with interface area in (a) heterodimer dataset (r = -0.88), and (b) class A (r = -0.92), however shows limited correlation in (c) class B complexes (r = -0.62).Figure S4: Solvation free energy gain upon interface formation (ΔClick here for fileiG in class B. Binding energies at the protein interfaces are highly correlated to solvation free energy gain upon interface formation (ΔiG) in the dataset (r = 0.88) and class A (r = 0.91), however shows limited correlation between BE and ΔiG in class B (r = 0.55).Figure S5: BE shows limited correlated with ΔClick here for file


Revealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection.The new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks.The experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively.The experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE. Protein-protein interactions (PPI) are fundamental to the biological processes within a cell. Beyond individual interactions, there is a lot more systematic information contained in protein interaction graphs. Complex formation is one of the typical patterns in this graph and many cellular functions are performed by these complexes containing multiple protein interaction partners. Many automatic approaches have been proposed to detect the protein complexes from PPI networks, such as CMC , COACH , MCODE , MCL 4]4], CfindAt present, large number of PPI databases have been created. Gavin , Krogan In this paper, we present a complex detection approach based on data integration and supervised learning. In this approach, the new PPI networks are constructed by integrating PPI datasets with the PPI data extracted by PPIExtractor  from bioIn our work, we use PPIExtractor  to extraPPIExtractor contains four modules: (i) Named Entity Recognition (NER) module which aims to identify the protein names in the biomedical literature; (ii) Normalization module which determines the unique identifier of proteins identified in NER module; (iii) PPI extraction module which extracts the PPI information in the biomedical literature; (iv) PPI visualization module which displays the extracted PPI information in the form of a graph. Figure http://www.ncbi.nlm.nih.gov/pubmed) with the query string "((Saccharomyces cerevisiae) OR yeast) AND protein" and PPIExtractor extracted a total of 126,165 protein interactions from these abstracts.127,217 PubMed abstracts were downloaded from PubMed website (http://www.uniprot.org/uniprot/? query=yeast&sort= score). The list is used to convert the protein names in PubMed abstracts to systematic names for nuclear-encoded ORFs.Since most of the protein names in the PPI databases are systematic names for nuclear-encoded ORFs begin with the letter 'Y' (for 'Yeast') while those in PubMed abstracts are not, we built a yeast protein alias name list with about 6,000 entries from the UniProt website| denotes the number of terms in C, the set of the GO terms in which annotation proteins m and n are included. | iT | denotes the number of terms in iT, the set of annotated proteins on GO term i gin whose annotation m and n are included. max Tdenotes the maximum size of annotated proteins on all GO terms. The GO term's specificity can be quantified by the proportion of the annotation size of a GO term ) to the total number of annotated proteins (maxT), i.e. a GO term is regarded to be more specific if it has less annotated proteins. NE denotes the number of neighbors that m and n share. The formula (1) demonstrates that if the GO term proteins m and n share is more specific, or if they have more common neighbors or GO terms, the interaction between them is more reliable. The details of the denoising PPI networks are shown in Table Where |PPIExtractor assigns the extracted PPIs from the biomedical literature weights representing their reliability . In our The weights of the PPIs added into the denoising PPI networks are higher than the integrating threshold -0.6. the reason is that our SLPC method have the best performance on the original PPI networks with the integrating threshold -0.6. What is more, the PPIs, when integrated into the denoising PPI networks, are also filtered with different denoising thresholds. The amounts of the PPIs added with different denoising thresholds are shown in Table In our work, a supervised learning protein complex detection (SLPC) method is employed to predict the protein complexes from PPI networks. Currently, most of protein complex detection methods are unsupervised ones, without utilizing the known complexes information. However, in the research field of protein complexes, numerous complexes have been provided, which can be used as the prior knowledge of the complex detection methods. In previous work, we presented a supervised learning protein complex detection (SLPC) method to predict protein complexes . The SLPWe constructed the gold standard protein complexes by combining MIPS , Aloy 1, SGD 1515 with TIn our study, F-score, Accuracy (Acc), maximum matching ratio (MMR) are used as the evaluation metrics. The neighborhood affinity score NA defined as follows is used to evaluate the similarity of two protein complexes A and B:If the NA is large than or equal to 0.25, complexes A and B are regarded to be matching.F-score, a popular metric of evaluating complex detection method, is used as the first measure to evaluate the performance.P and B are the predicted and gold standard complex sets, respectively; Ncb is the number of the gold standard complexes matching at least one predicted complex and Ncp is the number of the predicted complexes matching at least one gold standard complex and F-score is calculated as the harmonic mean of precision and recall values.Where Sn) and clustering-wise positive predictive value (PPV). A high Sn value indicates that the protein complex prediction has a good coverage of the proteins in the gold standard complexes, and a high PPV value indicates that the predicted protein complexes are likely to be true protein complexes. Assuming the number of the gold standard complexes is n and the number of the predicted complexes is m. ij Tdenotes the number of proteins that are found both in gold standard complex i and predicted complex j. The Sn, PPV, Acc are defined as follows:The second measure we used is the geometric accuracy as introduced by Broh´ee et al. , which iThe third metric we used is the maximum matching ratio (MMR) , which in denotes the number of the gold standard complexes; m the number of the predicted complexes; j as the member of the predicted complexes. MMR offers a natural, intuitive way to compare predicted complexes with a gold standard and it explicitly penalizes cases when a reference complex is split into two or more parts in the predicted set, as only one of its parts is allowed to match the correct reference [Where eference .The Acc measure explicitly penalizes predicted complexes that do not match any of the reference complexes. However, gold standard sets of protein complexes are often incomplete . As a coFirst we tested SLPC on three original PPI networks, i.e. DIP, Krogan and Gavin. The results of F-score, accuracy and MMR are shown in Tables However, Tables PPI datasets. And the denoising extended PPI networks are the ones into which the PPIs extracted from literature are integrated. More specifically, the new PPIs are also filtered out with different denoising thresholds like those PPIs in original PPI networks, and then integrated into the corresponding denoising PPI networks.Denoising PPI networks are the ones form which the low reliable PPIs are removed as discussed in the Section The performances of SLPC on denoising PPI networks are shown in Tables In addition, Tables The performance of ClusterONE, the state-of-the-art complex detection method, is also tested (its parameters are set as those described in ). With tProtein complexes, consisting of molecular aggregations of proteins assembled by multiple protein interactions, are of the fundamental units of macro-molecular organizations and play crucial roles in integrating individual gene products to perform useful cellular functions. Large amounts of PPI data generated by high-throughput experimental techniques can be used to predict protein complexes from PPI networks. At the same time, numerous accurate PPIs could be found in the rapidly growing biomedical literature since they are provided by biomedical experts. Their Integration with the existing PPI datasets can be hopeful to eliminate the PPI networks' sparsity, and, therefore, improve the complex detection performance.In this paper, an approach of introducing PPIs from biomedical literature into existing PPI networks and applying supervised learning method in protein complex detection is presented. In the approach, the new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection, SLPC, which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks.The best average improvements of 4.76, 6.81 and 15.75 percentage units in F-score, accuracy and MMR are achieved respectively, on original extended PPI networks. In addition, the best average improvements of 3.91, 4.61 and 12.10 percentage units in F-score, accuracy and MMR are achieved, respectively, on denoising extended PPI networks. All these results show that, the introduction of PPIs extracted from literature into the original PPI datasets can boost the performance significantly. The reason is that the sparsity problem of PPI networks is remitted by integrating PPI data from biomedical literature. The results also show that our method outperforms ClusterONE, the state-of-the-art method. This is because our method makes full use of the information of available known complexes. To summarize, our complex detection method, based on supervised learning method and integrating PPI data from biomedical literature, can achieve the better performances than other complex detection methods.The authors declare that they have no competing interests.ZHY and FYY conceived of the study, carried out its design and drafted the manuscript. FYY performed the experiments. FYY, XHH, HFL, and JW participated in its design and coordination, and helped to draft the manuscript. All authors read and approved the final manuscript.


P, the topological descriptor of the node’s ability to intervene in protein inter-module communication, was the key descriptor of PCN vulnerability of all structures. High P residues were localized both at PPIs and other regions of complexes, so that we argued an allosteric mechanism in protein–protein interactions. The identification of residues, with key role in the stability of PPIs, has a huge potential in the development of new drugs, which would be designed to target not only PPIs but also residues localized in allosteric regions of supramolecular complexes.Anthrax toxin comprises three different proteins, jointly acting to exert toxic activity: a non-toxic protective agent (PA), toxic edema factor (EF), and lethal factor (LF). Binding of PA to anthrax receptors promotes oligomerization of PA, binding of EF and LF, and then endocytosis of the complex. Homomeric forms of PA, complexes of PA bound to LF and to the endogenous receptor capillary morphogenesis gene 2 (CMG2) were analyzed. In this work, we characterized protein–protein interfaces (PPIs) and identified key residues at PPIs of complexes, by means of a protein contact network (PCN) approach. Flexibility and global and local topological properties of each PCN were computed. The vulnerability of each PCN was calculated using different node removal strategies, with reference to specific PCN topological descriptors, such as participation coefficient, contact order, and degree. The participation coefficient  Protein–protein interactions play a key role in the biological signaling pathways; however, they were considered difficult to study in terms of their chemo-physical and structural properties. For a long time, scientists simply relied on the functional properties of protein complexes, disregarding the physical details of protein–protein interactions. This restriction had drastically limited the therapeutic interventions impinging on such interactions. This situation changed because of increased structural information; therefore, drugs specifically designed to target protein–protein interactions have reached the market  at protein–protein interfaces (PPIs) are identified, PPIs become challenging pharmacological targets with a huge potential in structure-based drug design  has been recently applied to study protein complexes  bounded with lethal factor (LF) and CMG2. Furthermore, we analyzed monomeric and octameric PA. The analysis of PPIs for these complexes will open up the possibility to develop anthrax antidotes and possibly new anti-angiogenic agents.The attention on anthrax toxicity raised when, in 2001, 22 people were exposed to mail envelopes contaminated by anthrax spores  interactions of anthrax complexes.Analysis was focused on both global network stability  and local features . PCN approach has been further applied to evaluate complex stability, inferred from network resilience, or vulnerability  and ε is the set of links , the link describes a specific relationship between i-th and j-th nodes. Topological descriptors of a PCN quantitatively describe the network that corresponds to a given protein structure   of each node from other nodes. Indeed, high closeness centrality nodes are connected through few links to any other node of the network.Additionally, we computed two metrics of centrality:L of PCN is defined as the difference between the adjacency matrix A and the degree matrix D, which is a diagonal matrix, whose generic element Dii is the i-th node degree. The eigenvalue decomposition is applied to L; therefore, eigenvectors are ordered according to the descending order of the corresponding eigenvalue. The reference for the spectral clustering decomposition is the second minor eigenvalue 2v. The whole range of 2v values is divided into as many sub-ranges as the number of clusters (given as input); therefore, a node is placed in a cluster according to which sub-range its 2v value belongs.Furthermore, we applied a spectral clustering procedure in order to divide networks into clusters of nodes  show a peculiar shape, referred to as “dentist’s chair,” which represents the PCNs of the folded protein structures. The “dentist’s chair” shape is conserved for a large number of structures, and reveals a common architecture of protein folds regardless of protein function and size  varied from 8.7% (PDB: 3KWV) to 4.4% (PDB: 1T6B), whereas the inter-chain energy EC normalized per inter-chain contacts (EC/IC) substantially did not vary; meaning that EC only depends on the number of residues in the PPIs and not on their nature (contact length and type). Greater EC values described complexes bearing homomeric PA interactions (PDB: 3HVD and 3KWV).Global topological descriptors of PCNs Table  of monomProtective agent bound to LF (PDB: 3KWV) and to receptor CMG2 PDB: 1T6B) was analyzed  was also carried out. In 1T6B, a single PA chain is bound to CMG2. PA is divided into two intermingled functional domains, such as the yellow and the red clusters Figures A,B. The P  and PA–CGM2 (PDB: 1T6B) Figures  and 5. I Figures B and 5B e Figure D identiff (Table f = 0.8696), and 3HVD was the most rigid (f = 0.7143). Flexibility was high in monomeric forms of PA, and decreased with increasing number of interacting chains. In fact, flexibility was lowest in 3HVD where eight chains of PA are interacting. The comparison of flexibility of PA–PA–LF (PDB: 3KWV) and PA–CMG2 (PDB: 1T6B) revealed that the number of PA chains did not decrease the flexibility of 3KWV; although, the difference in values was not high.All structures showed a flexibility index f Table  >0.7, alP, contact order, and degree , which showed the biggest fall with 3% of removed nodes. The PA–PA–LF complex was less vulnerable than PA–CGM2; meaning that PA–PA interaction were not crucial in the participation coefficient P vulnerability strategy, at least for heteromeric interactions. The contact order removal strategy was substantially detrimental for network robustness of the octameric structure of PA (PDB: 3HVD) (7% of removed nodes) and of PA–PA–LF complex (PDB: 3KWV) (16% of removed nodes); in this case, PA–PA interaction influenced the robustness of PCNs. Finally, the degree removal strategy did not influence the robustness of all PCNs. The influence of P parameter was different in homomeric (PDB: 3HVD) and heteromeric complexes (PDB: 3KWV). Map of residues with high P values in the homomeric  that influence significantly the stability of complexes.Protein–protein interface properties were largely independent from global topology descriptor of the systems. For this reason, local (single residue) properties of PCNs were analyzed in detail.P, and the inter-chain degree. Furthermore, hot-spot residues with the highest betweenness centrality and the participation coefficient were localized in regions with allosteric functions. This result complies with previous works, hot-spot residues are not necessarily placed PPIs, and they can be localized in other regions of the complex acting through allosteric mechanisms  influencing the “hot-spot” character of residues. This result is in accordance with the previous findings about the modular feature of protein structures , the most vulnerable structure, high P residues were mostly localized at PPIs; this means that inter-module communication guide cooperative binding and influence PCN robustness. In the PA–PA–LF complex, high P residues were specifically at PA–LF and PA–PA interfaces and also in allosteric regions. Furthermore, PA–CMG2 complex was more vulnerable than PA–PA–LF based on P removal strategy. Thus, the participation coefficient P accounted for overall structural stability and high P residues were localized at PPIs and in allosteric regions. Furthermore, the participation coefficient P partially explained cooperative events. However, the parameter that mostly explained cooperative events was the contact order, because the most vulnerable structures were the octameric PA and the PA–PA–LF complexes, where there are homomeric PA–PA interactions. Interestingly, all analyzed structures were similarly resistant to node removal based on decreasing degree, which is known to be highly effective for scale-free networks (Holme et al., P residues, distributed both at PPIs and regions far from PPIs, as the most essential for global stability.Analysis of global PCN vulnerability identified which residues have a crucial role in the stability of anthrax complexes. The PCN vulnerability assessment showed that high P residues, crucial for supramolecular interactions, would have a perspective application as a guide for site-directed mutagenesis and other experimental approaches, aimed at functional characterization of PPIs. Furthermore, this approach will help medicinal chemists to identify cavities for molecular-docking studies, in order to design effective drugs targeting protein–protein interactions. Indeed, finding of high P residues will open additional scenarios to drug discovery applications. Drugs may not target PPIs, often scarcely accessible. Additionally, drugs would be designed to bind allosteric regions, influencing supramolecular interactions with efficacy comparable or higher than drugs targeting only protein–protein interfaces.The identification of high The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Structural comparison of protein-protein interfaces provides valuable insights into the functional relationship between proteins, which may not solely arise from shared evolutionary origin. A few methods that exist for such comparative studies have focused on structural models determined at atomic resolution, and may miss out interesting patterns present in large macromolecular complexes that are typically solved by low-resolution techniques.We developed a coarse-grained method, PCalign, to quantitatively evaluate physicochemical similarities between a given pair of protein-protein interfaces. This method uses an order-independent algorithm, geometric hashing, to superimpose the backbone atoms of a given pair of interfaces, and provides a normalized scoring function, PC-score, to account for the extent of overlap in terms of both geometric and chemical characteristics. We demonstrate that PCalign outperforms existing methods, and additionally facilitates comparative studies across models of different resolutions, which are not accommodated by existing methods. Furthermore, we illustrate potential application of our method to recognize interesting biological relationships masked by apparent lack of structural similarity.PCalign is a useful method in recognizing shared chemical and spatial patterns among protein-protein interfaces. It outperforms existing methods for high-quality data, and additionally facilitates comparison across structural models with different levels of details with proven robustness against noise.The online version of this article (doi:10.1186/s12859-015-0471-x) contains supplementary material, which is available to authorized users. Protein-protein interactions play important functional roles in almost all biological activities, including, but not restricted to, signal transduction, gene regulation, catalytic enzymatic activities and structural roles . CharactAt the center of protein-protein interactions are the binding surfaces, or interfacial residues which form contacts between binding partners and stabilize protein complexes. Characteristics of residues lining interfaces have been extensively studied, some focusing on their collective statistics such as hydrophobicity, buried surface area, depth index and planarity -6, otheret al. developed I2I-SiteEngine [Depending on the specific biological question being asked, methods for protein-protein interface comparison with different focuses have been developed. Galinter , for oneteEngine  to compateEngine , has alsGiven that there may be protein-protein interaction patterns that occur exclusively in large oligomeric complexes, there is significant added value in exploring these coarse-grained structural models when studying protein-protein interfaces, calling for an interface comparison method that can traverse through different resolutions of structural models.In this work, we develop a method that combines the advantages of existing methods to quantify the similarity of any given pair of protein-protein interfaces in terms of their physicochemical properties. This method not only disregards the sequence-order of interface fragments in performing the structural alignment, but also takes into account the mapping of different chemical types of amino acid residues. More importantly, our method facilitates comparison of structural models determined at different resolutions, greatly expanding the structural space of protein-protein interactions that can be studied systematically.While many structural models for protein monomers and homodimers can be determined at atomic resolution by X-ray crystallography or NMR techniques, the structures of large macromolecular complexes are typically solved by cryo-EM and are hence low in resolution. In spite of their lower resolution, these coarser structural models are nonetheless informative and can aid our understanding of protein-protein interactions. Statistics from PDB show over 600 structural models only contain information on the Cα atoms, with many of them populating the lower end of the resolution spectrum  that form intermolecular contacts from PDB. Similar to what Kolinski and Skolnick did in parameterizing pairwise interactions between side chain groups of different amino acids , our disi,j represent a given pair of residues of amino acid type i and of amino acid type j respectively . Meani,j and sdi,j represent the average values of the Cα-Cα distances in the set of high-resolution (37474) structures in the PDB and their standard deviation respectively. The statistics are listed in Additional file where After extraction of the interfacial residues, only the coordinates of the Cα atoms are retained for use in computing the pairwise interface similarity score, so as to allow comparison of interfaces with different levels of structural details. This bare bones criterion sufficiently captures the skeleton architecture that hosts amino acid residues of various chemical types at an interface, without adding noise to the data representation that arises from fluctuations of the side chain orientations.i is described by a feature defined by the Cartesian coordinates of the point, . The superposition that receives a high vote corresponds to one having many points that can be matched between the two sets of interfacial residues.In order to quantitatively assess the degree to which two sets of interfacial residues resemble each other spatially and chemically, we first superimpose the two sets of interfacial residues. As interfacial residues are fragments that are clustered at the binding site without these fragments necessarily following the peptide sequence order, we chose the sequence order-independent comparison technique, geometric hashing -18, to fThere are two additional factors to be taken into account in this problem; first is that an interface is not a single entity (a set of points) but rather consists of two binding fragments, A and B. Thus in aligning one interface to another, one needs to simultaneously align fragment A in interface 1 to fragment A’ in interface2, and B to B’, and not allow crossing over. To achieve this, we add to the feature  an additional attribute of fragment label (either binding site 1 or binding site 2), and count the votes only when both the coordinates and the fragment labels match. Without knowing the correspondence of binding fragments prior to alignment, we attempt both ways by swapping the fragment labels.et al. [In addition to the fragment label, another factor to be considered is the chemical label that is associated with each residue. Given the coarse-grained nature of our method (using only Cα atoms), we applied a reductive method to classify the 20 amino acids based on the prominent functional group in each side chain. The assignment of the individual functional groups in the side chain is based on the definition in earlier work by Schmitt et al. . DependiLastly, different pairs of orthogonal bases typically yield degenerate transformation matrices, and thus those receiving sufficiently high votes are further clustered to retain representative transformations as a last step, with the 100 top-ranking transformations processed for further refinement.The previous step proposed candidate transformations to superimpose the two interfaces initially, and in the iterative refinement step we aim to further improve the structural alignment in order to maximize the final similarity score. Based on each proposed initial alignment, a list of structurally equivalent pairs of residues between the two interfaces can be identified. Here we used maximum weight matching in bipartite graphs  to identith residue in interface 1 and jth residue in interface 2,In our particular problem, we’d like the weight to reflect spatially how close two residues are and also how well their chemical types match, and hence we’ve chosen the following scheme to quantify equivalence between Iij(same chem type) is the indicator function that takes the value of 1 when the pair of residues  share the same chemical type and 0 when they don’t. dij is the Euclidean distance between the Cα atoms of the two residues in Å after structural superposition. After obtaining the list of equivalent residues, we then apply the Kabsch algorithm [where lgorithm  to transraw, based on the converged alignment is given byThe overall scoring function, PC-scoreLave is the average number of interfacial residues for the pair of interfaces compared, and Lali is the number of all aligned residues identified by the aforementioned algorithm that have an equivalence-score of 0.20 or higher. fc is the ratio of common contacts between the two sets of aligned interfacial residues, and is calculated aswhere Lali ×Lali matrices representing the contact maps of the aligned interfacial residues in interface 1 and those in interface 2 respectively. The dot operation represents inner product. This scoring function is largely adapted from the scoring function of IS-score for the program Ialign [where m Ialign , given iFinally, the raw PC-score is further scaled by the following equation to remove the dependency of the score on the interface size to derive our final scoring function:This scaling function was derived by fitting the curve of the raw PC-score as a function of the size of randomly selected pairs of interfaces taken from a non-redundant representative data set derived from PDB  . The 124Our results show that our method performs comparably to the two existing methods in capturing most of the interface similarity reflected in the evolutionary relationship. As shown in Figure Iii (same chem type)), in our scoring function  as well as the RMSD between the aligned interfacial residues. Ideally, a good structural alignment program should find high coverage and low RMSD values. Note that this criterion is purely geometric. As shown in Figure As such, our method may detect more often than other methods highly similar but non-related interfaces that are counted as FPs in this benchmark test, which is the question of interest that our method aims to address and which cannot be accurately assessed by the classification analysis in Figure In terms of computational time, our method is slightly slower by less than an order of magnitude compared with Ialign and faster than I2I-SiteEngine , a protein involved in the ubiquitin-proteasome pathway, leading to degradation of the STAT1 protein . The latThe last example of viral mimicry involves the mechanism of viral entry into host cells. Nipah viruses employ their attachment glycoprotein G (NiV-G) for anchoring to the cell surface before initiating membrane fusion, specifically via binding of the glycoprotein G to ephrin-B2 ,43, whicWe also performed the same analysis using the two existing methods. While the non-sequential version of Ialign also detected significant similarity in all three cases, I2I-SiteEngine assigns scores that do not quantify these mimicked interfaces as sufficiently similar . In such situations the local signal becomes diluted out upon normalization against the average interface size. In this regard, caution needs to be taken when screening for small signature motifs embedded in large interfaces using our method that was designed for measuring global similarity among interfaces.With this new tool for protein-protein interface comparison, we would now like to expand our investigation of structural properties of protein-protein interfaces by analyzing large macromolecular assemblies, such as viral capsids. This class of proteins presents unique structural and functional characteristics unseen in cellular protein complexes , and thehttp://brooks.chem.lsa.umich.edu/index.php?page=software&subdir=articles/resources.The program PCalign, which operates on Linux systems, is freely available to academic users at


Arabidopsis thaliana, Auxin/indole-3-acetic acid (Aux/IAA) proteins repress auxin-inducible genes by inhibiting auxin response transcription factors (ARFs). Experimental evidence suggests that heterodimerization between Aux/IAA and ARF proteins are related to their unique biological functions. The objective of this study was to generate the Aux/IAA-ARF protein-protein interaction map using full length sequences and locate the interacting protein pairs to specific gene co-expression networks in order to define tissue-specific responses of the Aux/IAA-ARF interactome. Pairwise interactions between 19 ARFs and 29 Aux/IAAs resulted in the identification of 213 specific interactions of which 79 interactions were previously unknown. The incorporation of co-expression profiles with protein-protein interaction data revealed a strong correlation of gene co-expression for 70% of the ARF-Aux/IAA interacting pairs in at least one tissue/organ, indicative of the biological significance of these interactions. Importantly, ARF4-8 and 19, which were found to interact with almost all Aux-Aux/IAA showed broad co-expression relationships with Aux/IAA genes, thus, formed the central hubs of the co-expression network. Our analyses provide new insights into the biological significance of ARF-Aux/IAA associations in the morphogenesis and development of various plant tissues and organs.The phytohormone auxin regulates nearly all aspects of plant growth and development. Based on the current model in  The plant hormone auxin , regulates a wide range of developmental and physiological processes in plants including for example, apical dominance, root development, vascular differentiation, shoot elongation, and embryo patterning , which underlie the functional properties of these proteins. Domain I mediates the transcriptional repression of the proteins, whereas domain II mediates protein degradation  there are opportunities for numerous combinatorial interactions among these proteins to mediate specific responses.Characterization of plant responses to IAA treatments led to the identification of various classes of early auxin-responsive genes. Members of the Experimental evidence suggests that heterodimerization between Aux/IAA and ARF proteins are important to define their unique biological functions  to generate pGBKT7-ARFs. Similarly, PCR products of Aux/IAAs were digested, purified and fused to the GAL4 DNA activation domain of pGADT7 vector (Clontech) to generate pGADT7-Aux/IAAs. All constructs were verified by sequencing.Full-length coding sequences for 19 Saccharomyces cerevisiae strain AH109 was co-transformed with 551 pairs of pGBKT7-ARF and pGADT7-Aux/IAA vectors and yeast cells containing both vectors were selected using SD/-Leu/-Trp medium. The interactions between ARFs and Aux/IAA were assayed by plating the transformed cells onto the stringent SD/-Ade-His-Leu-Trp selective medium using at least 10 independent colonies. Serial dilutions of yeast co-transformed cells were used to measure the strength of the interaction.ARF5, 6, and 19 were PCR amplified using forward and reverse primers containing restriction enzyme sites  epidermal cells using particle bombardment as previously described by Hewezi et al. . Quality assessment of sequence reads was performed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Quality filtering and trimming were conducted using FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). After quality filtering and trimming, each sequence read was aligned to the TAIR10 Arabidopsis thaliana reference genome . Sample contribution scores were calculated by multiplying Z-score of ARFs and Aux/IAAs for each tissue as described in CoexViewer available at ATTED-II database   website. Sequences were aligned using ClustalX  assays , while Aux/IAA5, 6, 17, 32, and 34 were fused to the C-terminal half of a yellow fluorescent protein gene (cEYFP). Ten different combinations between nEYFP and cEYFP fusions were co-expressed in onion epidermal cells. All ARF-Aux/IAA interaction combinations, including those showing weak interaction in yeast (ARF6-Aux/IAA32 and ARF19-Aux/IAA34), reconstituted the fluorescent YFP in the nucleus of transformed cells  were cloned in a bait vector and full-length coding sequences of 29 Aux/IAAs (Aux/IAA1-20 and Aux/IAA26-34) were cloned in a prey vector. Yeast cells were co-transformed with 551 pairs of bait and prey vectors and potential interactions were visualized by differential growth on the non-selective synthetic dropout (SD) medium (SD/-Leu/-Trp) and on the selective medium (SD/-Leu/-Trp/-His/-Ade). An example of the interaction between ARF10 and Aux/IAAs is provided in Figure It is interesting to note that all ARFs that function as activators  were found to interact with all Aux/IAA proteins except ARF7 with Aux/IAA7 Figure . In contWe tested whether phylogenetically-related ARF and Aux/IAA proteins would have similar protein-protein interaction patterns. In general, we found that phylogenetically-related ARF and Aux/IAA proteins formed similar protein-protein interactomes. For example, ARF5-8 and ARF19, which are phylogenetically-related, interacted with almost all Aux/IAA proteins. Similarly, ARF10 and 16, which clustered together, were found to interact with the same Aux/IAA proteins including Aux/IAA5, and Aux/IAA32-34 .In order to map the co-expression events of the interacting ARF-Aux/IAA proteins to specific tissues or organs, the co-expression between pairs was determined using Z score as described in the Material and Methods. We set the “fragments per kilobase of transcript per million mapped reads” (FPKM) value of 2.0 as the threshold for expressed genes. As a result, cis regulatory element in the promoters of auxin-response genes, a middle region (MR) that functions as activator or repressor, and frequently a CTD involved in protein-protein interactions , interacted with almost all Aux/IAA proteins, a result that was also found in rice (Shen et al., ARF6 and 8 were found to co-express with various Aux/IAA genes only in flowers and floral buds. This finding is in agreement with the redundant function of ARF6 and 8 in regulating floral structures (Nagpal et al., While global protein-protein interaction networks provide an indication for all possible physical interaction combinations that can occur between proteins, the biological significance of such interaction requires the genes coding for these proteins to be co-expressed in particular cells. One approach to investigate the biological importance of such interactomes is to combine expression data with protein interaction data. This is because interacting proteins whose corresponding genes are co-expressed generally co-function in particular processes or pathways (Boruc et al., ARFs can mediate tissue-specific functions through their association with restrictedly expressed Aux/IAAs such as IAA33 and IAA34. It should be noted that several significant gene co-expression associations for the interacting proteins were identified in various tissues. This can be explained by the possibility that co-expression correlations may occur in various subsets of tissues or in specific cell types. In this context, analyzing the temporal and spatial gene expression patterns of the interacting protein pairs in the target tissues will precisely define their specific transcriptional signatures. On the other hand, we were unable to identify significant co-expression associations for about 30% of the interacting proteins in the tissues included in our analysis. The co-expression associations of these pairs may take place in specific tissues at particular developmental stages that are not included in our analysis. Alternatively, the co-functions of these pairs may be limited to particular physiological circumstances, including biotic and abiotic stresses.ARF4-8 and 19, which tend to interact with all Aux/IAAs were also found to be broadly expressed and hence constitute the main hubs in the interaction and co-expression networks Figure . Our datIn conclusion, our analysis confirmed most of the published protein-protein interactions between ARFs and Aux/IAAs and provided a new set of previously unknown interactions. In addition, the combination of co-expression data with protein-protein interaction data provided new leads to the site of the co-functions of ARF and Aux/IAA proteins. Taken together, these analyses set the stage for detailed functional analysis to reveal the biological significance of ARF-Aux/IAA interactions in the morphogenesis and development of various plant tissues and organs.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The functionality of most proteins is regulated by protein-protein interactions. Hence, the comprehensive characterization of the interactome is the next milestone on the path to understand the biochemistry of the cell. A powerful method to detect protein-protein interactions is a combination of coimmunoprecipitation or affinity purification with quantitative mass spectrometry. Nevertheless, both methods tend to precipitate a high number of background proteins due to nonspecific interactions. To address this challenge the software Protein-Protein-Interaction-Optimizer (PIPINO) was developed to perform an automated data analysis, to facilitate the selection of bona fide binding partners, and to compare the dynamic of interaction networks. In this study we investigated the STAT1 interaction network and its activation dependent dynamics. Stable isotope labeling by amino acids in cell culture (SILAC) was applied to analyze the STAT1 interactome after streptavidin pull-down of biotagged STAT1 from human embryonic kidney 293T cells with and without activation. Starting from more than 2,000 captured proteins 30 potential STAT1 interaction partners were extracted. Interestingly, more than 50% of these were already reported or predicted to bind STAT1. Furthermore, 16 proteins were found to affect the binding behavior depending on STAT1 phosphorylation such as STAT3 or the importin subunits alpha 1 and alpha 6. Proteins are team players. Virtually all protein functions are triggered, controlled, modulated, and conducted by protein complexes. Deregulation of protein complexes is the cause of many diseases as being documented, for example, cervical cancer, bacterial infection, leukemia, neurodegenerative diseases, and Huntington disease . ConsequUnfortunately, the information which protein complexes are formed and are present at certain conditions can not directly be obtained from transcriptome or genome data. Thus, the comprehensive analysis of protein-protein interaction networks (interactome) and its quantification and dynamics are one of the most important issues in the postgenomic era.In the last decades protein interactions were intensively investigated using small as well as large scale approaches. Much of the data is available and has been integrated in protein-protein interaction (PPI) databases such as Biological General Repository for Interaction Datasets (BioGRID) , the molOne of the most powerful methods for small and large scale PPI studies is affinity purification or coimmunoprecipitation combined with mass spectrometry –13. HoweTo experimentally identify true novel protein-protein interaction partners it is important to carefully conduct the PPI experiments including proper controls as well as to evaluate the experiments in respect to high quality data from previous studies. The data analysis should include statistical analysis of the raw data, data evaluation in respect of known contaminants such as beads binding proteins, and the recovery of already observed or predicted interaction partners. Additionally, putative PPI data can be filtered using functional information or correlation to large scale protein interaction networks. However, usually data analysis programs aim to filter the experimental data either using control experiments or by integration of functional information.http://www.bioforscher.de/pipino) is a novel attempt to integrate and combine the strengths of both approaches. PIPINO allows standardizing the data analysis process and offers a semiautomatic analysis pipeline. Beside various statistical methods for evaluating the data the software is capable of functionally annotating and enriching/filtering data entries with additional information. This refinement is accomplished by the use of curated interaction databases. Thus, a comprehensive interaction network can be created and used for the data analysis of a specific protein of interest. Database information and reliabilities in form of database scores for corresponding interactions can be used to assess the probability of the correctness of an interaction within the network through a normalized score as well as a visual representation. Additionally, it is possible to analyze and visualize perturbation of a network as it might be triggered by, for example, a bait phosphorylation. The performance of the software is demonstrated for the analysis of the interactome of the signal transducers and activators of transcription 1 (STAT1) with and without phosphorylation dependent activation. PIPINO is available at http://www.bioforscher.de/pipino.The software PIPINO  were washed with cell lysis buffer. After this equilibration step 50 For the investigation of activation dependent STAT1 interactions, three independent biological replicates were analyzed in two technical replicates. Therefore cell lysates of STAT1-Bio expressing cells, which were either treated with erythropoietin or left untreated, were compared. As a control, streptavidin pull-downs were performed from whole cell extracts of cells expressing either GFP-Bio and used as a third group.μg/mL 12C-L-arginine and 146 μg/mL 12C-L-lysine  were added to the “light” media while the same concentrations of  13C6-L-arginine or 13C615N4-L-arginine and 2H4-L-lysine or 13C6-15N2-L-lysine  were added to the “medium” or “heavy” media, respectively. After determining the time for full incorporation of the isotopic amino acids, HEK 293T cells were cultivated at least 6 days with SILAC medium before harvesting.In detail, cells were grown in SILAC Dulbecco's minimal essential medium ) without lysine and arginine, supplemented with 10% dialyzed fetal calf serum  and 1% penicillin/streptomycin . 84 μg/mL leupeptin, 5 μg/mL aprotinin, and 1 μg/mL pepstatin A were added freshly. Cells were incubated with RIPA buffer 10 min on ice, and extracts cleared by centrifuging for 10 min at 13,000 ×g and 4°C. Total protein concentrations were determined using a Bradford Assay.After washing the cells three times with PBS, whole cell extracts were prepared using modified RIPA buffer for affinity purifications . Protease inhibitors 5 For SILAC experiments, equivalent total protein amounts of light and heavy extracts were incubated separately with ultralink strep beads streptavidin beads for 1.5 hours. After 3 washing steps, bound proteins were eluted with SDS sample buffer, then combined, and subjected to SDS-PAGE.Samples from cells grown in heavy, medium, and light medium were mixed and 0.125 M Tris-HCl buffer containing 4% SDS, 20% (v/v) glycerol, 0.1% (m/v) bromophenol blue, and 10% (v/v) 2-mercaptoethanol was added. After heating the mixtures 5 min at 95°C the proteins were separated using 1D-SDS-PAGE (12%). After staining the proteins each lane was cut into 10 slices of approximately equal protein amounts. Proteins were destained and desalted within the gel slices and tryptic digestion was carried out using porcine trypsin.All protein digestions were analyzed by nano-uHPLC/nano-ESI-MS/MS using a LTQ Orbitrap XL ETD (Thermo) online coupled via a chip-based nano-ESI source  to a nano-uHPLC  as described before .http://www.expasy.org/, UniProt Consortium, 09-2010, 40924 forward and reverse sequences) was utilized. Thereby the protein and peptide false discovery rates were controlled to be below 0.05. For peptide identification up to a maximum of three isotope-labeled amino acids and maximum two tryptic missed cleavages were considered. A mass error of up to 0.5 Da for MS/MS product ions and up to 20 ppm for MS precursor ions was tolerated. Methionine oxidation, acetylation (protein N-terminus), asparagine and glutamine deamidation , and cysteine carbamidomethylation (complete) were considered as modification. On request the complete GeLC-MS raw data as well as details concerning identifications and quantifications will be provided. All raw quantitation ratios as well as the results of all filtering and statistical analysis steps of the three conditions are summarized in Supplementary Table 1 .Mascot  . It is capable of visualizing and analyzing data and supports the selection of bona-fide interaction partners based on literature data such as protein interaction networks, frequency data, and bead proteome lists. The application is written in pure Java and is available as a standalone version including a detailed description at In this study MS raw data were processed and protein abundance ratios were calculated by ProteomeDiscoverer (Thermo Sci.). However, this is not a prerequisite for the application of PIPINO. In fact, initial quantitative proteomics data analysis by any other software or search engine can be used.For a successful import of user data, the software requires a column separated data format containing experimental data  paired with general information regarding the experiment . The parser is flexible enough not to demand a special data format. The knowledge of the concrete data format and where needed information is located are sufficient to import data. This input system enables to use common shared data formats, for example, generated by ProteomeDiscoverer (Thermo Sci.), MaxQuant  (MPI MunUser data is parsed and converted into a uniform intermediate data format understandable by the software. This can be achieved through a step-by-step transformation that utilizes regular expressions (RegEx). An example of a transformation can be found in the Supplementary Information.First the document needs to be structured separating the header from the content and specifying field delimiters. The numbers of different experiments and samples per experiment (replicates) in the document are required in this step. The source columns of the user's document (characterized by the header fields) can be mapped to the target data model fields of the software. In this mapping either the whole data field or just a fragment specified through an extraction pattern in form of a regular expression can be used. A preview shows the outcome of the model conversion on a few data rows and indicates whether values have been calculated properly. If the transformation result is satisfying and valid the conversion parameters can be stored as a template for upcoming data imports of the same data source. Finally the complete experimental data will be converted to the intermediate data format and represented in a tabular form.Insufficient entries  or known nonbinding partners can be removed via accession numbers or gene names in an optional filter process. Additionally, a predefined list of bead proteomes  and a liri,j and the probability value pi,j are calculated for every protein Pi and experiment Ej. These values are used as core features for data analysis purposes. A calculation starts with the value Vi,j,k of a corresponding experiment Ej and sample Sj,k.The enrichment ratio Sj,k  against a zero vector is utilized. Proteins with less than two samples for an experiment are considered as insufficient and therefore neither enrichment ratios nor probability values are calculated:A precomputational step calculates the mean Sj,k  for eachan Vi,j  of all sn values . The norio ri,j  and the j  can be interpreted as quality measure of the data and is applied on the ordinate. The logarithmic application of the enrichment ratio compensates the scattering of the data regarding the p values. Thus the plot is divided into a depleted part to the left of the ordinate and an enriched part to the right. As a result, all data points in the first quadrant of the diagram have been found enriched in the experiments. In general the more distant a data point can be found to the abscissa the more likely is its accuracy. As multiple experiments can be present in a single file, PIPINO is capable of switching between these experiments when displaying the volcano plot.As intermediate data is present, a visualization procedure can be initiated with the volcano plot to get an overview of the data distribution. The diagram applies the logarithmic enrichment ratio on the abscissa. The probability value  supports the analysis of AP-MS data by facilitating (i) raw data processing, (ii) interactive data visualization, (iii) comparison with data from PPI databases, and (iv) comparison with lists of proteins frequently observed in AP-MS experiments or known to bind nonspecifically and by providing additional network presentations . The curTo map the STAT1 interactome with and without phosphorylation an AP-MS strategy was used as descripted recently in Blumert et al. . It is bThe carboxy-terminus of STAT1 or GFP (for control) was fused to a 23-amino acid peptide tag carrying a target sequence for biotin protein ligases (biotag) and was coexpressed with the codon-optimized bacterial biotin protein ligase variant hBirA for in situ biotinylation in HEK 293T. Translation and biotinylation efficiency were monitored by Western blotting and visualized using streptavidin horseradish peroxidase conjugates. GFP-Bio and STAT1-Bio were found to be expressed in comparable amounts. For both proteins the biotinylation efficiency was not dependent on the amount of coexpressed hBirA or on the amount of biotin. Thus, it was considered to be stable and complete.Cytokine-dependent tyrosine-phosphorylation and transactivation potential of either untagged STAT1 or STAT1-Bio were examined to verify that the STAT1 functionality was not affected by biotinylation. Because HEK 293T express only marginal levels of functional interleukin-6 receptor which is required for STAT1 activation, a chimeric receptor EG consisting of the extracellular domains of the erythropoietin receptor and the transmembrane and cytoplasmic parts of the interleukin-6 signal transducer gp130 was coexpressed, and cells were stimulated by adding erythropoietin (EPO) to the medium to activate STAT1. After EPO stimulation and overexpression of either STAT1 or STAT1-Bio the abundance of tyrosine-phosphorylation of STAT1 and STAT1-Bio was found to be equal.13C6-Arg and D4-Lys), and cells expressing STAT1-Bio and which were additionally stimulated with EPO were labeled with “heavy” amino acids (13C615N4-Arg and 13C615N2-Lys). An equal number of cells per condition were lyzed, bait/prey complexes were enriched by affinity chromatography, and the eluates were mixed. The combined eluates of the streptavidin pull-downs were separated by one-dimensional SDS-PAGE and the proteolytic peptides which were generated by tryptic in-gel digestion were analyzed using nano-HPLC/nano-ESI Orbitrap mass spectrometry. In total three biological replicates were measured in two technical replicates.A triple labelling SILAC strategy was applied to relatively compare the quantities of captured prey proteins between the control bait (GFP-Bio) and the target prey without activation (STAT1-Bio) and with EPO activation (pSTAT1-Bio). Therefore HEK 293T cells overexpressing GFP-Bio were labeled with “light” amino acids (Arg and Lys), cells expressing STAT1-Bio were labeled with “medium” amino acids  in at least one of the six measurements. A file containing a list of all proteins including protein database identifiers and the relative quantification results between the three different channels  was exported.2-transformed, and (iii) mean values and significance (t-test) are calculated. Furthermore, all proteins not quantified in at least three measurements  were removed. Thus, in case of the STAT1 dataset 963 reproducibly quantified proteins (quantified in three replicates based on at least two peptides of which one was required to be unique) remained for further analysis once 1220 proteins have been sorted out.After parsing the data with an appropriate template (specified in the Supplementary Information) an initial data analysis can be conducted. In this step (i) technical replicates can be combined, (ii) optionally the biological replicates can be normalized and logDatabase information on proteins being frequently copurified such as endogenously biotinylated proteins (6 proteins) and typically bead binding proteins as well as proteins which are known to bind proteins involved in protein folding or degradation such as chaperons and proteases 19 proteins) was integrated and flagged to reduce the candidate list. The remaining 942 proteins were further investigated. Furthermore, a list of proteins which are frequently observed in numerous pull-down experiments as reported, for example, by Boulon et al. [ proteinsParticularly for most of the human proteins but also for proteins of many other species there is information on known interaction partners listed in several databases. Automatic access to this information is highly valuable since this data can be used to judge the quality of the purification, to guide the following filtering steps, and to prevent redundant reporting of the so-called novel PPIs. Homo sapiens comprises 201.110 interactions between 22.306 proteins.PIPINO allows the integration of all standard databases such as BioGRID , MINT 55, MenthaThis raw network was centered on STAT1 computed from the interaction databases MINT, IntAct, and Mentha entries for which interaction scores are available. The network was enriched by the altered experimental data to create a specialized network containing 17.568 proteins including 1240 proteins from the experiment, of which 1230 were part of the applied database. 10 proteins could not be found in the interaction databases. Further refinements of the network through truncation resulted in 1.575 remaining proteins. The possible interaction partners as well as the unconfirmed proteins in general can now be subjected for further analysis and studies.For each of the filtered proteins  the information is provided whether and in which databases the proteins have been reported as bait binding partner.p values can be utilized to further enrich potential protein interaction partners. The number of background proteins as well as the relative quantities of the captured binding partners can have high variations in AP-MS or Co-IP-MS experiments. Thus, to define and apply ratio or significance thresholds is not as straightforward as it is in standard proteomics applications. To support the definition of thresholds the processed data can be interactively visualized by the use of the integrated volcano plot as has been depicted in As an additional filtering step the data can be analyzed using the quantification information. In the STAT1 example the enrichment of all captured proteins is quantified compared to the pull-down of biotinylated GFP. The ratio of these quantities as well as 2 FC > 1 and a p value < 0.005. For the pSTAT1 dataset 15 proteins fulfill these additional criteria. With a proportion of > 50% well known STAT1 binding proteins are highly enriched. For the STAT1 dataset 17 proteins are part of the defined significance region of the volcano plot. Interestingly, only 2 of those have been already reported.The volcano plots of either the STAT1-Bio versus GFP-Bio  or the pAt each time point and after every adjustment of a filtering criterion the result can be visualized using three different types of network presentations . FurtherThe sophisticated overlay of interaction databases and experimental data helps to identify interaction pathways between proteins. It is possible to detect currently uninvestigated protein interactions as well as to identify possible protein hubs within the experiment. The information provided by the network analysis can be used to further encircle possible interaction partners while excluding irrelevant proteins.In case of the pSTAT1 dataset using the truncated network proteins are visualized which either do directly interact with STAT1 or are only separated by one or two nodes . These pParticularly the investigation of differences or dynamics in protein networks which are caused by drugs, toxic compounds, external stimuli, mutation, and so forth is an upcoming topic. In this study we investigated alterations caused by a cell treatment with EPO. EPO treatment caused a STAT1 phosphorylation, activation, and translocation to the nucleus. Thus one can expect a huge impact to the STAT1 interactome. Since all three conditions were processed and analyzed simultaneously it is possible to directly determine in a single analysis whether a protein binds STAT1 (enrichment in either the STAT1 or pSTAT1 pull-down compared to the GFP control pull-down) and if the same protein does bind differentially to STAT1. One protein which is well known to form heterodimers with STAT1 after phosphorylation but not without phosphorylation is STAT3 , 25. As A semiautomatic analysis revealed that in total 30 proteins were found to bind either STAT1 or pSTAT1 . Of thesAmong the six proteins that showed stronger binding without activation are two proteins being involved in degradation , two mitochondrial enzymes (electron-transfer-flavoprotein and NADH dehydrogenase (ubiquinone) Fe-S protein 3), the phosphofructokinase, and the coiled-coil-helix-coiled-coil-helix domain containing 3, which has been reported to be important for protein import in mitochondria but also to act as transcription factor and to regulate the BAG1 promotor. Interestingly, two known STAT1 binding partners the cytoskeletal protein spectrin  and espeAP-MS is one of the most powerful approaches to identify protein interactions and can be applied for high-throughput studies. Several analysis tools have been designed to facilitate and automatize the identification of protein-protein interactions based on AP-MS data (recent review ) such asp values  discard Perseus ), and in Perseus , or STRI Perseus ). HoweveWe concluded that especially in cases in which in-depth analysis of the protein interaction network of one or only a few selected baits is investigated it might be beneficial to automate the data processing and provide the researcher a maximum of additional data and visualization options. Based on these different types of information which are quantitative MS data, reproducibility of the AP-MS measurements, detection frequencies in previous experiments, biological functions, presence in PPI repositories, and so forth, the researcher has the possibility to define own thresholds and to report and prove the putative protein interactors.p values, and contaminant lists, an interference analysis with standard or manually curated PPI databases as well as pathway analysis within a bait centric network and the PPI data export. Particularly useful for a fast modification of data processing process is the fact that the results can be visualized as an iterative volcano plot or in three different network presentations.Therefore PIPINO is designed for an initial processing of small datasets (few or even only one bait) by the use of interactive visualization and evaluation. The software allows a full processing starting with raw quantification data as has been exported by, for example, MaxQuant, ProteomeDiscoverer, PLGS, and Biotools, and is conducting all steps including data normalization, filtering based on fold changes, Thus, the compact and user-friendly interface integrates all modules in a single application and is prepared for upcoming modules to be integrated as well. Therefore workflows and pipelines can be kept simple and straightforward. Due to the use of Java, the software can basically run on all operating systems and is not limited to Windows or Linux. Furthermore there is no complicated installation routine necessary, PIPINO can be started from within its folder, and therefore it is even possible to execute it as a portable application.Using PIPINO the phosphorylation dependent STAT1 interactome studied by a single-step triple-SILAC based AP-MS approach was analyzed in detail. Starting from more than 2000 captured proteins it has been possible to finally extract a list of 30 potential interaction STAT1 partners of which more than 50% were already reported. Interestingly the semiautomatic analysis with PIPINO also revealed that 16 proteins were found to change the binding behavior depending on the STAT1 phosphorylation state such as STAT3 or Importin subunits alpha 1 and alpha 6.PIPINO can be used as an effective and supportive tool for analyzing protein-protein interactions obtained from experimental methods located in the field of affinity purification and mass spectrometry based quantitative proteomics. The software is capable of processing a large amount of data formats while enlarging the information space through curated interaction databases. Filtering, annotating, categorizing, and visualizing data entries, respectively, possible interaction partners for a protein of interest are available as a solid basis for interaction analysis.Enhanced algorithms for network considerations, database handling, information extraction, and data refinement are currently under development and evaluation. These novel approaches will further increase the accuracy of current methods, enhancing the usability of the software and reducing needed efforts for the user in analyzing possible protein-protein interactions.I. Input parameter for the application to the STAT1 interaction network.The header size of 1 and a single tabulator as field delimiter was chosen to structure the document. The amount of experiments was set to 3 with 6 samples per experiment. The accession number could be retrieved directly with the default extraction pattern “(.∗)”, while the gene name was extracted from the description field through the extraction pattern “GN=(.∗) P”. The taxonomy id was set manually to “9606” (Homo sapiens). The three experiments have been named manually and the associated sample values could be retrieved directly from appropriate fields. All adjusted parameters have been saved as a template for the ProteomeDiscoverer with three experiments and six samples and therefore can be loaded for upcoming parsing assignments.Supplementary Table: AP-MS data of all proteins being quantified in this study. Additionally, for each protein the fallowing information are provided:.