Protein-protein interactions are ubiquitous and essential for all cellular processes. High-resolution X-ray crystallographic structures of protein complexes can reveal the details of their function and provide a basis for many computational and experimental approaches. Differentiation between biological and non-biological contacts and reconstruction of the intact complex is a challenging computational problem. A successful solution can provide additional insights into the fundamental principles of biological recognition and reduce errors in many algorithms and databases utilizing interaction information extracted from the Protein Data Bank (PDB).We have developed a method for identifying protein complexes in the PDB X-ray structures by a four step procedure: (1) comprehensively collecting all protein-protein interfaces; (2) clustering similar protein-protein interfaces together; (3) estimating the probability that each cluster is relevant based on a diverse set of properties; and (4) combining these scores for each PDB entry in order to predict the complex structure. The resulting clusters of biologically relevant interfaces provide a reliable catalog of evolutionary conserved protein-protein interactions. These interfaces, as well as the predicted protein complexes, are available from the Protein Interface Server (PInS) website (see Availability and requirements section).Our method demonstrates an almost two-fold reduction of the annotation error rate as evaluated on a large benchmark set of complexes validated from the literature. We also estimate relative contributions of each interface property to the accurate discrimination of biologically relevant interfaces and discuss possible directions for further improving the prediction method. Proteins usually accomplish their biological functions as components of stable complexes or through transient interactions with other proteins. The most detailed experimental information on protein complexes comes from high-resolution X-ray structures. These structures provide clues on the mechanism by which the complex accomplishes its function, give insights into the physical and evolutionary principles of protein-protein interactions through statistical analysis -4, and cBecause protein crystals contain a regular array of molecules it is a nontrivial task to determine which molecules form the biologically relevant complex. Only a subset of the molecules in the asymmetric unit or additional molecules related by space group transformations may be included in the complex. Although Protein Data Bank (PDB) files contain information on generating the biological complex (BIOMT records), this information is error prone. An approximate lower bound, which is discussed in the Results section, suggests that the error rate for BIOMT annotation is at least 9%. This means that structural studies, such as those mentioned above, must depend on either small but reliable manually curated sets of protein complex structures or on large but significantly less reliable automatically generated sets.Computational methods that predict the biological complex for an X-ray structure can improve the reliability of complex annotations. The earliest methods were applied only to the more limited problem of distinguishing homodimers from monomers by using a variety of differentiating features: interface area and atomic pair contacts , fractioWe have developed a new method for identifying biological relevant protein complexes in X-ray structures and demonstrate a significant reduction in the annotation error rate. In addition, our database (the Protein Interface Server (PInS), see Availability and requirements section for URL), contains a useful byproduct – a new classification of all protein contacts  found in the PDB. First, protein-protein interfaces are grouped by similarity and all interfaces in a particular cluster are either all predicted to be specific  contacts or non-specific  contacts. Crystal contacts are defined to be protein-protein interfaces that only occur in the non-physiological environment of the crystal . Because all interfaces in a cluster containing biological interfaces are between proteins with similar amino acid sequences and have similar binding geometry, they presumably arose through evolutionary divergence. Thus they provide a valuable resource for studying how similar interfaces appear in different complexes of diverse functions. Second, the protein-protein interface clusters are classified by a machine learning method that utilizes a robust set of diverse interface properties. Previously we demonstrated that a similar set of properties was sufficient to distinguish near-native docked conformations in a large set of decoys . The comA non-redundant benchmark set of 435 complex structures was created by combining the sets of  and [3],0/(solvent excluded surface area), in which SASA0 is the SASA with a zero probe sphere radius and the solvent excluded surface is calculated using a 1.4 Å radius probe sphere. The evolutionary conservation of each residue was defined as the column entropy S for a multiple alignment of similar sequencesProtein-protein interfaces were predicted to be either biologically relevant contacts or crystal contacts using a Random Forest classifier  trained fi are the residue frequencies in the corresponding alignment column. The multiple sequence alignment was generated by collecting similar sequences from the NCBI nr database using BLAST [i.e. possesses two-fold crystallographic symmetry, was included in the interface properties because it is a common feature of homodimers.in which ng BLAST  with theng BLAST , and finng BLAST . The evong BLAST . FinallyClustering procedure section). Previous studies have used various criteria to cluster protein-protein interfaces by similarity [Similar protein-protein interfaces were clustered into groups. Essentially those groups are groups of identical  structures, where corresponding residues are globally aligned. All interfaces in a particular group are assigned to be either all crystal contacts or all biological interfaces  in each protein-protein interface was calculated with the residues referred to by their uniform residue number. All original protein chains as well as symmetry-related chains within 25 Å of any of the original chains, with the inter-chain separation defined by the minimum distance between non-hydrogen atoms in each chain, were included in order to insure all interfaces for the complete biological complex are present. The symmetry-related molecules were generated using PyMOL .Interfaces were then clustered so that the minimum fractional residue contact overlap O, defined byN(interface i contacts) denotes the number of contacting residue pairs in interface i and the numerator is the number of common contacting residue pairs. This overlap cutoff value is actually a rather strict requirement as residues in the interfaces make multiple contacts with each other, so that when 30% of all contacts are the same between two interfaces it is very unlikely that the interfaces are unrelated. Although clustering a large number of interfaces would be computationally expensive, the clustering problem can be subdivided into manageable subproblems. First identical interfaces, with the same proteins and same relative space transformation, within each PDB complex were rapidly identified and grouped. Next, all non-identical interfaces throughout the PDB were clustered using hierarchical complete linkage clustering with distance measure 1-O and the largest clusters with O ≥ 0.3 were selected. This task was subdivided by clustering only groups of interfaces with the same pair of Pfam accession numbers since the overlap between interfaces with different Pfam numbers is zero by definition. This clustering reduced the 254879 non-identical protein-protein interfaces into 58274 clusters. Identical protein-protein interfaces, which have the same residue-residue contacts between the same proteins, have fractional residue contact overlap O = 1.0 and are therefore trivially assigned to the same cluster.was at least 0.3 between any two interfaces in the same cluster. Protein-protein interface prediction data.The Random Forest method uses the consensus prediction from an ensemble of randomized decision trees for classification . It was Each protein-protein interface was classified as either a specific contact appearing in a biological complex or a non-specific crystal contact. The variation of the Random Forest score within each cluster was estimated by calculating the standard deviation in the score within a sample of interfaces of intermediate size. All 93 clusters containing exactly 20 interfaces were chosen for this purpose. The average of the standard deviation in the scores within each cluster was found to be only 0.04. Because the intra-cluster score variation is so small and also to speed up the calculation, predictions were made for only a single randomly chosen interface in each cluster and all interfaces in the cluster were assigned to the same class, as described above. A total of 1000 classification trees generated from 20 random chosen variables were used in the Random Forest classifier. As expected , the preA Random Forest score was calculated as the fraction of trees that classify the interface as a biological interface. All other interfaces in the same cluster, which were not used with the Random Forest classifier, were then also assigned this score.Prediction performance was assessed using 10-fold cross-validation. The 2006 interface clusters appearing in the benchmark set were randomly divided into 10 approximately equal size test sets. Predictions were then made for each test set in turn using a Random Forest classifier trained on the remaining interface data in the other 9 sets and the prediction statistics calculated. This procedure prevents overly optimistic performance estimates due to overfitting. Predictions for structures not included in the benchmark set were made using a Random Forest classifier trained on data for all interface clusters in the benchmark set.One further advantage of the Random Forest classifier is the ability to quickly estimate the contribution of each input variable to the overall prediction accuracy. This is accomplished by calculating the average decrease in accuracy for data upon permuting the values for the variable of interest. This can be efficiently calculated because only the so-called out-of-bag data is used (data not included in the bootstrap sample) . BecauseOverall, the prediction procedure for the protein complexes combines local information from the Random Forest interface prediction scores with global information from the number of subunits in the BIOMT complex using a consistent probabilistic framework. The prediction is performed by maximizing the total probability over all structures simultaneously so that, in general, information from multiple structures contributes to the prediction of each individual complex.Pspecific (S)) and crystal contact interfaces (Pnon-specific (S)) in the benchmark set using gaussian kernel density estimation. The resulting smooth distributions are shown in Figure The Random Forest scores for each interface in a predicted complex need to be converted into probabilities in order to calculate the total likelihood that the complex is correct. This was accomplished by separately fitting the score distributions for biological interfaces  by computing the following product over all unique interface clusters in the predicted complex.This equation assumes that the probabilities for each unique interface are independent. This is a very good approximation, because we cluster all similar interfaces together, and interfaces in a cluster are either all assigned as specific or all assigned as non-specific. It is not possible to consider as independent closely related interfaces within clusters.Even though the BIOMT annotation in the PDB files, which specifies how to generate the biological complex, is error-prone, it is still often correct and thus provides valuable information for predicting the complex. Therefore a predicted complex whose oligomeric state agrees with the BIOMT annotation should be assigned a higher likelihood to be correct than one with a different oligomeric state. We have chosen the number of subunits in the complex as a criterion of such agreement because often the only independent experimental information on the complex, aside from the crystal structure itself, is its oligomeric state as measured by, for example, chromatography.NBIOMT) agrees with the number of subunits in the actual complex (N) isThe probability that the number of subunits in the BIOMT complex  is the number of predicted complexes with i subunits out of all possible predicted complexes.in which N can agree with NBIOMT: either the BIOMT complex is correct (first term in Equations 4 and 5) or the BIOMT complex is incorrect and the number of subunits agrees by chance (second terms in Equations 4 and 5). The value of P(BIOMT correct) is approximated by its upper bound due to consistency (0.91) discussed above. The BIOMT contribution, PBIOMT, to the overall likelihood of the predicted complex can be then calculated based on the number of unique subunits observed in the prediction as P(N = NBIOMT) from Equation 5, if N = NBIOMT, or 1 - P(N = NBIOMT), if N ≠ NBIOMT.There are two different ways in which PBIOMT,  and the local interface contribution, P({Si}), in Equation 3. The total likelihood of a particular interface assignment is the product of the probabilities over all complexes considered.The probability for a particular complex is calculated as the product of the BIOMT contribution, i.e. biological contact) or non-specific  interfaces between the two corresponding subunits. For a particular assignment of the interfaces, the component of the graph that (1) is connected by specific contacts, (2) contains at least one subunit in the asymmetric unit, and (3) contains all unique subunits is the predicted complex. Because the interfaces in a cluster are considered either all specific or all non-specific there are Nclusters is the total number of clusters.The set of protein complex subunits and resulting protein complexes may be represented by a graph in which each node corresponds to a particular subunit and edges join contacting subunits. There are two types of edges in the graph: specific  are considered.The assignment of interface clusters as either specific or non-specific and the resulting predictions of biological complexes are then accomplished by maximizing the total likelihood over all possible interface cluster assignments subject to the aforementioned constraint. Because there are 58274 clusters and the number of possible interface assignments is This is done by generating a graph in which each node represents a cluster and edges are placed between two nodes for clusters co-occurring in any structure. Next, all connected components in this graph are determined. Independent optimizations are then performed for each set of interface clusters corresponding to nodes present in a single connected component. The optimization was done by exhaustive enumeration of all interface cluster assignments in subproblems with ≤ 15 interface clusters and otherwise using a Monte Carlo algorithm employing Metropolis sampling with a temperature parameter of 0.4. The vast majority of subproblems (92%) could be exactly solved by the former method.The performance of the Random Forest classifier in predicting protein-protein interfaces was assessed by plotting the Receiver Operator Characteristic (ROC) curve for the 10-fold cross validation results on the benchmark set. The ROC curve, shown in Figure As described in the Methods section, the importance of each interface property to the overall Random Forest prediction accuracy can easily be calculated. This can give insight into which properties are most useful in discriminating specific protein-protein interfaces from non-specific interfaces and provide guidance for future prediction efforts.First we compare the relative importance of individual properties using the out-of-bag data. The properties making the largest contribution to the prediction accuracy, along with their relative importance, are listed in Table 2 [The interface area makes the largest contribution to the prediction accuracy. Its importance in discriminating specific protein-protein interfaces has been noted in earlier studies ,12,37. O2 , smallerThe number of intermolecular hydrogen bonds gives the second largest contribution (55%) to the prediction accuracy. This is expected since it is highly correlated with the interface surface area ,39, withThe 210 residue contacting pair counts and the 20 residue propensities, each considered as groups of variables, contributed 22.4% and 18.4%, respectively. Examining the importance the propensities for each residue type reveals that the largest contributions are from leucine, phenylalanine, isoleucine, tyrosine, and valine, in decreasing order of importance. A previous study that used the same statistic as a measure of the propensity of particular residue types to appear in protein-protein interfaces also found that these were some of the most prevalent in interfaces [The performances of different prediction methods were evaluated by counting the number of correctly predicted protein complexes having the correct stoichiometry, or oligomeric state. A total of 46 out of the 435 benchmark set complexes were incorrectly predicted, resulting in an error rate of only about 11% for our method. The error rate for the subset of 214 benchmark set complexes taken from the Ponstingl et al. 2003 set was 13%. This is slightly lower than the 16% error rate reported in that study. We also calculated the error rates for PQS and PISA predictions for comparison. One potential difficulty with this is that both of these databases contain multiple predictions for some PDB entries. This is not a significant problem for PQS since only 3 of the 75 PDB entries in the benchmark set with multiple predicted complexes had different oligomeric states, which is the basis for our evaluation of prediction results. Only the most stable predicted complex for each PDB entry was used to calculate the error rate for PISA, and furthermore only the total number of subunits in each complex was compared, because only these results were available for automatic download. There were between 75 and 78 incorrect predictions for PQS, depending on which of the multiple predicted complexes were chosen for comparison, yielding an error rate of approximately 17%–18%. A total of 104 of the predicted PISA complexes disagreed, giving an error rate of approximately 24%. Unfortunately, this value is not directly comparable with that for our method for the reasons mentioned above. However, the PQS error rate is significantly higher, even after accounting for the few PDB entries with multiple predicted structures.i.e. have different stoichiometry. A further breakdown of these complexes by type is shown in Table A comparison of the predicted protein complexes with those generated from the PDB annotation revealed that a total of 17% of the predicted complexes are different, The prediction method for protein complexes also yields clusters of related protein-protein interfaces found in biological complexes. These predicted Pfam-A domain-domain contacts were compared with those in the iPfam  and 3DIDIndeed, the iPfam database includes all contacts, both specific and non-specific and the 3DID database used an empirical residue contact score to remove possible non-specific contacts. Even more importantly, neither of these databases includes symmetry-related chains so that many specific contacts are necessarily missing as they simply are not included for consideration. Our algorithm aims to include only specific contacts in predicted biological complexes, and we take special care to enumerate all contacts observed in the X-ray crystal structures by applying the necessary symmetry operations. Of course, biological applications relying on these databases are mostly interested in the biologically relevant domain interactions rather than crystal contacts.A searchable database of all predicted protein complexes is available online (see Availability and requirements section for URL). Search fields include the PDB entry name, text in the complex or subunit description, interface cluster number, or the Pfam accession number. The complex structures matching the query are presented both explicitly as three-dimensional structures and schematically as graphs in which nodes represent subunits and edges represent contacts  and due to fundamental difficulties of differentiating between biological and non-biological contacts. Separate questions arise from the presence of incomplete protein complexes. In those cases the crystallized assembly is only part of true biological complex. The PDB BIOMT annotation is known to contain a large number of incorrect annotations, and two other previously developed approaches  provideOur method has demonstrated an almost two-fold reduction of the error rate for predictions on a large set of 435 protein complexes manually assigned through analysis of available biological literature. The error rate for our method was 11% as compared with error rates of 17–18% for PQS and approximately 24% for PISA. There was also a smaller reduction in the error rate for a subset of protein complexes from Ponstingl et al. 2003 from 16% for that method to 13% for the method described here.Another result of our work is the clustering of similar interfaces. The enforced similarity guarantees that two parts of the interface not only have similar biological sequences, but also have similar spatial arrangements. The groups are created for all contacts found in the PDB . The availability of interface clusters opens several possibilities for further analysis. For example, it is possible to check the literature for biological evidence of a particular interface by using sources related to all PDB complexes containing it. This could greatly increase our confidence in the annotation of the difficult cases, both on the level of individual interfaces and on the level of complete complexes. Further analysis of these interface groups and how they co-occur in complexes may give insights into the evolutionary history of protein complexes.An analysis of the contribution of each interface property to the accurate Random Forest prediction of protein-protein interfaces showed that the interface area, number of intramolecular hydrogen bonds, evolutionary conservation, over-represented  interface residues and residue-residue contacts, and interface packing density contributed the most. An important advantage of the Random Forest method over other machine learning methods is that it can use all of these diverse properties, without adjusting their relative normalization, in order to make an accurate prediction.The observed reduction of the error rate is both significant and valuable for applications, but in our opinion it is also surprising that it remains relatively high. In our study we used a comprehensive set of interface characteristics, but obviously more has to be done to understand what governs contacts under biological conditions. Several further directions for study are possible. One of the most interesting would be to combine our machine learning approach with free energy calculations as suggested by the approach used for the PISA database . AnotherFinally, we found relatively little agreement on which Pfam domains form contacts in protein complexes among complexes predicted from our method and those in the iPfam and 3DID databases. We attribute this discrepancy mainly to crystal contacts and missing subunits in the other databases due to ignoring symmetry-related chains. It would be interesting to also compare the set of interacting Pfam domains in the complexes identified by our method with those predicted from yeast two-hybrid data .Protein Interface Server (PInS):  AB conceived the prediction methodology and carried out the calculations. Both authors participated in the analysis of the results, database website design, and drafting the manuscript. Both authors read and approved the final manuscript.Benchmark set of 435 protein complex structures with prediction results. The file is a tab-separated table with the following columns: PDB entry; correct oligomeric state; data source ; oligomeric state of the complex predicted by our method; oligomeric state of the complex predicted by PQS; total number of subunits in the complex predicted by PISA; and oligomeric state of the PDB BIOMT complex.Click here for file


PROCARB is an open-access database comprising three independently working components, namely, (i) Core PROCARB module, consisting of three-dimensional structures of protein-carbohydrate complexes taken from Protein Data Bank (PDB), (ii) Homology Models module, consisting of manually developed three-dimensional models of N-linked and O-linked glycoproteins of unknown three-dimensional structure, and (iii) CBS-Pred prediction module, consisting of web servers to predict carbohydrate-binding sites using single sequence or server-generated PSSM. Several precomputed structural and functional properties of complexes are also included in the database for quick analysis. In particular, information about function, secondary structure, solvent accessibility, hydrogen bonds and literature reference, and so forth, is included. In addition, each protein in the database is mapped to Uniprot, Pfam, PDB, and so forth.Understanding of the three-dimensional structures of proteins that interact with carbohydrates covalently (glycoproteins) as well as noncovalently (protein-carbohydrate complexes) is essential to many biological processes and plays a significant role in normal and disease-associated functions. It is important to have a central repository of knowledge available about these protein-carbohydrate complexes as well as preprocessed data of predicted structures. This can be significantly enhanced by tools  Carbohydrates play a key role in a variety of important biological recognition processes like infection, immune response, cell differentiation, and neuronal development. All of these biological phenomena may be regulated by the interaction of these carbohydrates with proteins –4. One aIn spite of their numerous important biological roles, there is no appropriate database dedicated to these protein-carbohydrate complexes. Although, the Protein Data Bank (PDB)  stores aStructure-based approach to drug design has become a standard protocol in the pharmaceutical industry where large databases of potential small drug candidates may be docked into an active site of a particular target molecule . StructuFinally, functional annotation of proteins and understanding of functions in cases were only an amino acid sequence of protein is available requires predicting potential carbohydrate-binding sites, which experimentalists can then verify. Based on our previous work in this direction , we deveOverall organization of the database is illustrated in Figures The PROCARB core module is developed by systematically locating protein-carbohydrate complexes in the protein data bank (PDB) and manual verification of existence and identification of carbohydrate ligand. A protein is considered as a carbohydrate binding if any atom of its amino acid is within a 3.5 Å cutoff distance from any atom of the sugar in the protein-carbohydrate complex . VariousFASTA formatted sequences and 3D coordinates for both raw and nonredundant datasets are also stored in the database. These data sets are scheduled to be regularly updated as new entries become available from the PDB. For a quick analysis a set of four residue-wise structural features, namely, contact with carbohydrate, secondary structure, and solvent accessibility is included. These features are computed using standard software such as DSSP , ASAViewInformation on each complex is stored in an MYSQL database where the central protein table contains information regarding the protein, its bound ligand, function, and literature reference. Web interface uses PHP and JavaScript and allows searches by a variety of text-based options like PDB code, ligand name, protein name, and source organism. Data entries are displayed using dynamically generated pages which describe the relevant information including protein name, source, ligands, Pfam  descriptIn this module, we have attempted to generate the three-dimensional structures of a large number of glycoproteins (both N- and O-linked) with hitherto unknown structure, using automated web-based homology modeling. As a case study, a detailed project model-based 3D-structure of Hev b 4, a latex allergen N-glycoprotein has also been completed which is described elsewhere in our earlier work .To select proteins for modeling, Swissprot  search wSelected glycoprotein sequences, having at least one experimentally verified glycosylation site, were used as an input for the web server 3D-JIGSAW . This seThough this is based on using automated web-based homology modeling, most of the models are within the acceptable ranges of Ramachandran score Tables and 4 andMany proteins which interact with carbohydrates  are known without the knowledge of residues that participate in these interactions. Only few computational methods have been described till date which predict the covalently attached Glycosylation sites , 41 in pWe also tested the CBS-Pred on Area under the ROC curve (AUC)  for protPROCARB30: A nonredundant dataset of protein-carbohydrate complexes submitted to PDB between January 2007 and November 2008.PROCARB61: A redundant dataset of protein-carbohydrate complexes submitted to PDB between January 2007 and November 2008.A BLAST  sequenceDue to the enormous diversity of carbohydrates, it is always difficult to identify whether a given ligand in a PDB coordinate file is a carbohydrate or not. Carbohydrate Finder identifies diverse types of carbohydrates in a given protein-carbohydrate complex. Currently, it can recognize 100 different types of carbohydrates.Contact Calculator calculates the contacting pairs in a given protein-carbohydrate complex at different cutoff distances and can also recognize 100 different types of carbohydrates that may be in contact with the amino acid residues . A database of protein-carbohydrate complexes and models of unknown glycoprotein structures was developed, and an associated sequence-based prediction module was compiled. We expect that PROCARB will facilitate functional annotation, designing of site-directed mutagenesis experiments, and modeling protein-carbohydrate interactions which in turn will help the experimental and bioinformatics research on understanding protein-carbohydrate interactions.http://www.procarb.org or http://procarb.netasa.org.PROCARB can be accessed at


Pseudomonas putida KT2440 (P. putida KT2440) is a highly versatile saprophytic soil bacterium. It is a certified bio-safety host for transferring foreign genes. Therefore, the bacterium is used as a model organism for genetic and physiological studies and for the development of biotechnological applications. In order to provide a more systematic application of the organism, we have constructed a protein-protein interaction (PPI) network analysis system of P. putida KT2440.P. putida KT2440 which is generated from three protein-protein interaction (PPI) methods. We used PSIMAP , PEIMAP , and Domain-domain interactions using iPfam. PutidaNET contains 3,254 proteins, and 82,019 possible interactions consisting of 61,011 (PSIMAP), 4,293 (PEIMAP), and 30,043 (iPfam) interaction pairs except for self interaction. Also, we performed a case study by integrating a protein interaction network and experimental 1-DE/MS-MS analysis data P. putida. We found that 1) major functional modules are involved in various metabolic pathways and ribosomes, and 2) existing PPI sub-networks that are specific to succinate or benzoate metabolism are not in the center as predicted.PutidaNET is a comprehensive interaction database and server of P. putida KT2440 PutidaNET is freely available at http://sequenceome.kobic.kr/PutidaNET.We introduce the PutidaNET which provides predicted interaction partners and functional analyses such as physicochemical properties, KEGG pathway assignment, and Gene Ontology mapping of  P. putida KT2440 is a ubiquitous bacterium which can break down a variety of organic materials for food. Because of its versatile metabolic activities, P. putida KT2440 is thought to play a pivotal role in the recycling of organic wastes and the degrading of biogenic and xenobiotic pollutants in the environment http://ww as BIND , MINT [1 as BIND , HPRD [1 as BIND , CYGD) [ as BIND , and Bio as BIND . PSIMAP  as BIND -23. In t as BIND  with a c as BIND  domains http://string.embl.de[In order to select more reliable PPIs, we developed and used a 'combined score' between any pair of proteins which were predicted by PEIMAP, PSIMAP, and iPfam algorithms. This scoring method is also used by the STRING server g.embl.de.P. putida KT2440 proteome, we searched physicochemical properties and cross-reference databases using KEGG and GO. We used Biopython [P. putida KT2440 proteome.In order to understand the biological function of iopython  modules iopython  and Signiopython  programsPutidaNET provides cross-reference to public database information such as 1) KEGG pathways, 2) GO categories, and 3) GO-slim  through P. putida KT2440 was pre-cultured at 30°C with vigorous shaking in culture media  and then inoculated into 1 L culture media containing succinate (10 mM) or benzoate (5 mM) as a sole carbon source. The bacteria were harvested at the late exponential phase (absorbance at 600 nm. 0.7-0.8) and suspended in 20 mM Tris-HCl buffer (pH 8.0). Bacteria were disrupted by a French pressure cell  at 20,000 lb/in2, and soluble protein mixtures were prepared by centrifugation . The protein samples were fractionated by 12% SDS-PAGE. The gel lanes were divided into 42 fractions according to molecular weight, and the sliced gels were digested with trypsin . The resulting peptide extracts were pooled and lyophilized in a vacuum concentrator. Tryptic peptides were dissolved with 0.5% TFA (Trifluoroacetic acid) solution prior to further 2D-LC fractionation and used for MS/MS analysis using LTQ linear Ion Trap MS . For the database search, the P. putida protein database was downloaded from the National Center for Biotechnology Information http://www.ncbi.nlm.nih.gov/. Tryptic peptides were identified using SEQUEST .In order to find significant features, we integrated PPI network and proteomic data which were produced as previously described . P. putiP. putida protein database and the reverse protein database were used to exclude any false-discovered proteins [For better accuracy of protein identification by MS/MS analysis, the proteins .We acquired the protein lists in culture media including succinate or benzoate. In order to find regulated sub-network by succinate and benzoate, we analyzed betweenness centrality (BC), the number of shortest paths going through a certain node, and degree, the number of interaction partners, using NetworkAanalyzer, a cytoscape plugin . We usedP. putida KT2440, contains 82,019 PPI partners that have been predicted. Using the PPI algorithms, we obtained 61,011 (PSIMAP), 4,293 (PEIMAP), and 30,043 (iPfam) predicted PPIs except for self interaction. These PPIs were around 74.39% (PSIMAP), 5.23% (PEIMAP), and 36.62% (iPfam) of the P. putida KT2440 proteins. Although the total number of predicted interaction targets is very large, as they are ranked by combined score, experimentalists can select high ranking (more probable) ones according to their functional interests.PutidaNET, a free accessible database with 3,254 proteins for P. putida KT2440 [P. putida KT2440. PPIs were regulated specifically by difference sets of benzoate and succinate that tend to occur at the network periphery more than the network center  and rich medium (LB) also showed that the major induced protein patterns were very similar (data not shown). Specifically induced proteins in benzoate medium were β-ketoadipate pathway enzymes for benzoate and 4-hydroxybenzoate and stress proteins. On the other hand, enzymes for TCA cycle, pyruvate metabolism, and glycolysis were increased in succinate medium, which will be increased for the utilization of succinate influx.Figure a KT2440 . And we P. putida KT2440 which has a high level of metabolic diversity for biodegradation. This high level of diversity enables the bacterium to utilize a wide range of carbon sources. The ribosome is an organelle that coordinates protein synthesis in all cells. The bacterial ribosome consists of more than 50 ribosomal subunit proteins and three rRNAs. Since bacterial cells contain vast amounts of ribosomes, most ribosomal subunit proteins can be observed as main peaks by mass spectrometry.In order to find features in protein networks, we detected functional modules as highly interconnected sub-networks. As a result, we found five functional modules with KEGG pathway information Figure , Table 1Pseudomonas putida. The PutidaNET server is the first web server that provides various kinds of functional information such as a PPI viewer, physicochemical properties, biological pathways, gene ontology, and protein-protein interaction for P. putida KT2440. It can assist researchers to access and obtain the information through an automatic annotation for queried proteins. Using proteomics data from certain medium conditions, we analyzed the characteristics of P. putida KT24400 using PutidaNET. Proteomic data gave us the quantitative information of induced proteins at benzoate or succinate culture conditions, which supplements the database. PPI combined with proteomic data can give users more specific information.PutidaNET is the integration of mutually complementary protein-protein interaction information for the systematic analysis of The authors declare that they have no competing interests.SJP, BCK, and JWR constructed the database. SWC and DP developed the web site. JSC designed the overall web site. KAL analyzed the statistics of PPI network results. SJP, JSC, DP, and JWR wrote the main draft of the paper. JB and SIK directed the study and helped with the draft manuscript.BMC Bioinformatics Volume 10 Supplement 15, 2009: Eighth International Conference on Bioinformatics (InCoB2009): Bioinformatics, available online at http://www.biomedcentral.com/1471-2105/10?issue=S15.Other papers from the meeting have been published as part of Supplementary Figure 1-(a-e) the biological modules obtained from MCODE cytoscape plug - in. This figure shows five functional modules: a) module 1, b) module 2, c) module 3, d) module 4, and e) module 5. For example, module 1 is a functional module about ribosome pathways.Click here for file


Annotation of protein functions is an important task in the post-genomic era. Most early approaches for this task exploit only the sequence or global structure information. However, protein surfaces are believed to be crucial to protein functions because they are the main interfaces to facilitate biological interactions. Recently, several databases related to structural surfaces, such as pockets and cavities, have been constructed with a comprehensive library of identified surface structures. For example, CASTp provides identification and measurements of surface accessible pockets as well as interior inaccessible cavities..A novel method was proposed to predict the Gene Ontology (GO) functions of proteins from the pocket similarity network, which is constructed according to the structure similarities of pockets. The statistics of the networks were presented to explore the relationship between the similar pockets and GO functions of proteins. Cross-validation experiments were conducted to evaluate the performance of the proposed method. Results and codes are available at: The computational results demonstrate that the proposed method based on the pocket similarity network is effective and efficient for predicting GO functions of proteins in terms of both computational complexity and prediction accuracy. The proposed method revealed strong relationship between small surface patterns (or pockets) and GO functions, which can be further used to identify active sites or functional motifs. The high quality performance of the prediction method together with the statistics also indicates that pockets play essential roles in biological interactions or the GO functions. Moreover, in addition to pockets, the proposed network framework can also be used for adopting other protein spatial surface patterns to predict the protein functions. It becomes an increasingly important task to annotate protein functions when more and more protein sequences and three dimensional structures become available . A fundaIt is well known that protein functions are mostly determined by physical, chemical and geometric properties of protein surfaces , becauseIn this paper, to develop a new method to predict protein functions, we aim to exploit the surface structure information by constructing a pocket similarity network. We choose the CASTp  databaseFor the protein functions, we focus on the prediction of Gene Ontology (GO) terms . GO is aCross-validation experiments are conducted to evaluate the performance of the proposed method. Numerical results are demonstrated in the recall-precision graphs, which verify high effectiveness of the proposed method for both prediction accuracy and computation efficiency. Such computational results together with statistical analysis also reveal that similar surface pockets in proteins are essential to biological activities and are strong clues to similar GO functions. In contrast to the existing approaches which are mainly suitable for homologous proteins, the proposed method is not only effective for the proteins with distant relationships or with convergent evolution, but also can further reveal direct links between small surface patterns and GO functions, which actually can be explored to identify active sites or functional motifs.As well known, there are many redundant (due to different experiments) or similar (due to same protein family) structures in PDB, which result in biased statistics. In order to eliminate the unexpected side effects, the PDB_SELECT 25  was chosFor each protein in the dataset, we retrieved all of its pockets from CASTp  when avaThe similarity of two pockets was evaluated by pvSOAR server . An examGO annotated node if the pocket is from a GO annotated protein. Similarly, an edge is called GO annotated edge if two endpoints of the edge are both annotated. An annotated edge is called a GO related edge if two endpoints of the edge are annotated by at least one common GO term.In the pocket similarity network, a node or pocket is called All data used in the experiments were retrieved from the related websites, and the versions of databases are as follows: CASTp (2006-01-20), pvSOAR (2006-07-13), GO (2006-09-01) and GOA (2006-05-31).In order to explore the relationship between the pocket similarity and the GO terms similarity, we calculated the percentage of the GO related edges, i.e., the percentage of the pocket pairs of which the two corresponding proteins have at least one common GO term. The results in several pocket similarity networks constructed by different thresholds are listed in Tables -1 (loosest) to 1.0 × 10-5 (tightest) from left to right. The second row indicates the numbers of pocket pairs that satisfy the given threshold, i.e. the numbers of edges in the pocket similarity network. The third row lists the GO annotated edges, i.e. the pocket pairs which are both from GO annotated proteins. Some of the edges are not GO annotated edges, and therefore removed in the next experiments. The fourth row shows how many GO annotated edges in the given pocket similarity network are GO related, i.e. the corresponding proteins of such an edge have at least one identical GO function. The last row is the percentages of GO related edges, which are obtained by dividing the corresponding numbers in the fourth row by the numbers in the third row. The percentage is the statistical significance of the pocket pairs with at least one identical GO function in all GO annotated pocket pairs.In these tables, the first row gives the similarity thresholds, which are ranged from 1.0 × 10Obviously, when the thresholds become tighter, i.e. two pockets in an edge become more similar, the probability of the corresponding proteins with at least one identical GO term increases. The tendency becomes more significant when the structure similarities such as cRMSD and oRMSD are used instead of the sequence similarity as the threshold, which are illustrated in Tables Figure We also computed the frequencies of GO functions in the GO related edges. The GO functions in a GO related edge are the common GO functions of two proteins corresponding to the edge. The top 15 functions found from a pocket similarity network  according to the score scheme presented in section Methods. Then the correspondence scores between the target protein and GO terms were inferred based on the scores of its pockets.The recall-precision graph of the prediction is shown in Figure The original (i.e. unfiltered) predicted result has recall value 0.774 and precision value 0.706. In the 316 testing proteins, there are 267 proteins, each of which has at least one GO term predicted in the experiments. The prediction method does not hit any GO term of the rest 49 proteins. The coverage is 84.50%, which is considerably high and quite efficient for predicting protein functions. The result illustrates that the presented method can rather correctly predict GO functions for most proteins, even if they come from different protein families, or there is no homologous information available.For the prediction results of individual proteins, most proteins have high recall and precision values as shown in Figure Since Gene Ontology is composed by three relatively independent ontologies, the prediction results on the three ontologies are summarized respectively. The three recall-precision graphs are shown in Figure -3 to 10-5, the recall-precision curve moves to a higher position, and the prediction coverage and accuracy also increase simultaneously. For instance, if the threshold is 10-3, the maximum F-measure increases to 0.875, the recall is 0.888 and the precision is 0.839. One obvious disadvantage for a tight threshold is that the number of proteins involved in the pocket similarity network is fewer than that with a loose threshold. Such a tradeoff between performance and scale is shown in Table We also did computational experiments on the pocket similarity networks featured by different thresholds. The results are similar to those in this section. When the threshold of the pocket similarity network becomes tighter, e.g. from 10We also predicted the GO terms for the 81 un-annotated proteins and verified the predicted results by the functional information from other sources such as PDB and literature. The predicted results are almost consistent with the existing knowledge. Some results for un-annotated proteins are listed in Table In order to evaluate and justify the significance of pocket similarity, we constructed the protein similarity network by using a similar method as that used in the construction of the pocket similarity network. The proteins in the same dataset were compared all-against-all using CE algorithm . In the -2 and the Z-Score 4.8, while Figure -3 and the Z-Score 5.8. Additional comparisons between different cRMSD p-values and Z-Scores can also be found in Additional File Figure The GO organizes the terms as directed acyclic graphs (DAG), where the child term is more specific and informative than its ancestors. Generic terms do have less relevance for comparing the functional similarity between proteins. Therefore the presence of unspecific GO terms may bias the statistics and prediction results. Two experiments are conducted to evaluate the influence of the unspecific GO terms. In the first experiment, the GO semantic similarity -36 of eaIn the second experiment, the unspecific GO terms are discard from the prediction results. The relevance information of GO terms are represented by their probability  to occuranno(c) is the number of proteins annotated with the term c, and children(c) is the set of child nodes of term c. The probability of the term c is then defined as p(c) = freq(c) = freq(root), where freq(root) is the frequency of the root term 33] and SSome proteins are not annotated in current GOA database, and their prediction results are partly shown in Table The main point of this paper is utilizing local structure information to improve the effectiveness and accuracy of protein function prediction. It is also very interesting and important to check those functionally similar proteins which have local structure similarity instead of global one to find the functional sites in detail and identify their functions. Our main concern is current data are far from complete to do this. Comprehensive experiments on complete local structure library and larger structure databases such as whole PDB may be necessary to archive interesting and convincible results for detecting functional sites. Also the prediction accuracy may be further improved. Our future attempts will eventually take into account the detailed local structural properties related to protein function.In conclusion, a novel prediction method was proposed in this paper to predict protein GO functions from the surface pocket similarity, by directly linking structural patterns with biological functions. In addition to the high coverage and accuracy of the predictions, the prediction method is also simple and computationally efficient, and therefore can be applied to large-scale problems. The statistics and computational experiments show the effectiveness of the method. It is a supplement to the existing prediction methods based on sequence and/or global structure similarity. In contrast to the existing methods which are mainly effective for homologous proteins in divergent evolution, the method in this paper is also suitable for the proteins with distant relationship or with convergent evolution.The pocket similarity network is a network in which each node represents a pocket and each edge connects two similar pockets. For each considered protein, all its pockets can be obtained from CASTp database. Then an edge is linked between a pair of nodes, if their similarity measure satisfies the given threshold. The pvSOAR is an all-against-all comparison database of pockets in CASTp. For each similar pocket pair, the pvSOAR database provides three statistical significant measurements: E-value of sequence similarity, p-value of structure similarity cRMSD, p-value of structure similarity oRMSD. These three measurements are explained in ,28 and tThe correspondence scores between a pocket and GO terms are determined by its closest neighbors. The closest neighbors of a pocket are those pockets directly linked to the considered pocket. The occurrence number of each GO term in all closest neighbors is recorded in the scoring table, in which each row represents a considered pocket and each column represents a GO term. Then the scores between the considered pocket and GO terms are assigned by the frequencies of GO terms, which are the normalized occurrence numbers of GO terms, i.e. each number in the scoring table is divided by the sum of elements in the row that it is located. The scoring scheme for a pocket and GO terms is illustrated in Figure After the scores are calculated for all pockets of the target protein, the correspondence scores between the protein and GO terms are inferred from the scores of pockets. For a considered GO term, the weighted sum of the correspondence scores of all pockets in the target protein is the score of the protein with regard to the GO term. An example is illustrated in Figure The prediction performance of the proposed method is evaluated by some widely used measurements in information retrieval research, such as recall, precision and F-measure. The evaluation is usually displayed in a recall-precision graph. And the F-measure can be used as a single measure of performance of the test, which is the harmonic mean of precision and recall. Mathematically, these measurements are defined as follows.where the TP, FP and N are abbreviations of the number of true positive, number of false positive, and number of false negative respectively. The global performance is evaluated by using leave-one-out cross-validation experiments. The specific evaluation of prediction performance on each individual protein is also calculated in the similar way.PDB: protein data bankcRMSD: coordinate root mean square distanceoRMSD: orientation root mean square distanceCASTp: computed atlas of surface topography of proteinspvSOAR: pocket and void surfaces of amino acid residuesGO: gene ontologyGOA: gene ontology annotationRP: recall-precisionR & P: recall and precisionTP: true positiveFP: false positiveFN: false negativePMID: PubMed identifierLYW and YW proposed the original idea, ZPL and LYW designed the details and implemented the experiments, LC and XSZ improved the methods and give valuable suggestions. All authors wrote and approved the manuscript.Illustration examples and additional results. The illustration examples of the pocket similarity network and the similar pockets are provided. The file also includes additional statistics and prediction results.Click here for file


Protein-protein interactions are at the basis of most cellular processes and crucial for many bio-technological applications. During the last few years the development of high-throughput technologies has produced several large-scale protein-protein interaction data sets for various organisms. It is important to develop tools for dissecting their content and analyse the information they embed by data-integration and computational methods.Interactions can be mediated by the presence of specific features, such as motifs, surface patches and domains. The co-occurrence of these features on proteins interacting with the same protein can indicate mutually exclusive interactions and, therefore, can be used for inferring the involvement of the proteins in common biological processes.We present here a publicly available server that allows the user to investigate protein interaction data in light of other biological information, such as their sequences, presence of specific domains, process and component ontologies. The server can be effectively used to construct a high-confidence set of mutually exclusive interactions by identifying similar features in groups of proteins sharing a common interaction partner. As an example, we describe here the identification of common motifs, function, cellular localization and domains in different datasets of yeast interactions.The server can be used to analyse user-supplied datasets, it contains pre-processed data for four yeast Protein Protein interaction datasets and the results of their statistical analysis. These show that the presence of common motifs in proteins interacting with the same partner is a valuable source of information, it can be used to investigate the properties of the interacting proteins and provides information that can be effectively integrated with other sources. As more experimental interaction data become available, this tool will become more and more useful to gain a more detailed picture of the interactome. Protein functions are mediated and regulated through a complex network of interactions . In manyThe recent development of high-throughput technologies for detecting protein-protein interactions (PPIs) has produced many publicly available databases ,7-10. AlIn this work we describe a server for simplifying the analysis of the features shared by proteins interacting with the same partner. We show here its power by investigating the presence of sequence motifs in yeast PPI maps and their correlation with the presence of similar Gene Ontology annotations (process and component)  and PfamThe MoVIN server can load experimental PPI datasets and perform an analysis of sets of interactions sharing a common partner Figure . It contGiven a dataset, the tool automatically extracts the groups of proteins sharing a common interaction partner (see Methods). These are the sets of all and only the proteins that bind to a common protein partner. This central protein (hub) is used to identify the cluster. The user can select the minimum size of the cluster. For each cluster, MoVIN collates the corresponding set of protein sequences and searches them for the presence of common motifs using MEME  and MASTMEME (vers. 3.5.3) is a tool for discovering motifs in a group of related DNA or protein sequences. MEME takes as input a group of sequences (in this case the sequences of the proteins in a cluster) and outputs as many motifs as possible according to the constraints given by the user. MEME uses statistical modelling techniques to automatically choose the best length and description for each motif. In MoVIN we use as background distribution the average composition of the complete set of proteins in the map and focus on motifs with length between three and twelve residues and with an E-value (as reported by the output of the program) lower than a user-defined threshold (default is 10e-7). To avoid that motifs with a strong signal (e.g. fingerprints of a domain shared by some proteins of the group) mask “weaker” motifs, the server repeats this step on each cluster many times, recursively eliminating the proteins for which a common motif has already been identified.In order to estimate the specificity of the motifs found by MEME, they are mapped on all the proteins in the dataset using MAST (vers. 3.5.3). For each motif, MAST returns a list of proteins containing it, the position of the match in the sequence and the corresponding E-value. Only matches with an E-value smaller than a user-defined threshold  are retained.i and each motif Mj a motif P value Si,j related to the over-representation of the motif in that cluster. If the motif has been found on xij protein in the cluster Ci of ni proteins and on Xj proteins in the complete dataset of size N we have Smi,j=hgcdf where hgcdf is the hypergeometric cumulative distribution function, which measures the probability of finding at least as many occurrences of the motif in a cluster of similar size randomly extracted from the whole set of proteins.Next, the server assigns to each cluster Cj. Different motifs can be present on a protein and one protein of a cluster can be, at the same time, the hub protein of another cluster, therefore different scores can be associated to the same interaction. In such cases, we assign the minimum score to the interaction.Finally, the previously calculated scores are assigned to the interaction between the central protein of the cluster and each protein in the cluster containing the motif Mprocess P value Sp, the component P value Sl and the domain P value Sd are computed in a similar fashion. The GO process score and the GO component score are calculated by mapping the corresponding GO ontology terms on each protein with the program BatchGoViewer, which returns, given a list of proteins, the annotations (at any level) with the lowest P values. The Pfam domain score is calculated by analysing the presence of Pfam A Domains on each protein. The protein-domain relationship is taken from the Ensembl website [The  website .The results of all the analyses are displayed with a user-friendly interface, including textual search and graphics tools. The cluster and their features are graphically visualised using GraphViz. For each displayed item there are links to several different publicly available databases.Additionally, the user can look for the presence of known mutations and for their position in the highlighted proteins (as reported in the Protein Mutant Database) . It is pWe used MoVIN to analyse the BioGRID, BIND, Gavin06 and Krogan datasets Table . We onlyj, bj) is present we can not have the pair ). We randomly shuffle all the second terms of each pair and remove duplicate interactions. By doing so, we preserve the connectivity degree of each protein and hub proteins remain such in the randomized map. The total number of interactions of each randomized map differs by less than 1% from that of the original map.First of all, we analysed the motif P value distribution generated by the server using as input the original datasets and then compared them with the corresponding background distribution. Such background distribution is obtained -for each PPI map- by randomizing the set of interactions of the original map (written as ordered pairs) with no duplicate , therefore we can compute the standard Z-score of our original dataset:dM is the mean of the distribution on the original dataset, M is the mean and σ is the standard deviation of the mean distribution. The randomization step was repeated 5 times for each dataset, yielding each time similar results (data not shown). The results  and the motif-selected dataset is biased towards mutually exclusive interactions, it is likely that our selected subset is enriched in true positive interactions, and can be a good candidate for applications that need a benchmark interaction dataset.The MoVIN server can also use other sources of information commonly used to assess the reliability of interaction data, i.e. GO annotations (process and component) and Pfam domains. An important question is to which extent the motif information overlaps with that given by the co-occurrence of GO terms and the presence of similar Pfam domain in proteins interacting with a common partner.To address the issue, we applied the method to the same yeast datasets described above. Here we only report the results for the BIND dataset, the results for the other datasets are very similar (data not shown).10  and the P values for GO process, GO component and Pfam . An alignment of the sequences is reported in Figure The hub protein YKL074C and 4 proteins in the group  have a GO process annotation that is related to the spliceosome. Another protein (YLR357W) has an annotation (double-strand break repair via nonhomologous end joining) which is consistent with the splicing pathway. The two proteins YBR172C and YPL105C are homologous and both contain the motif. The second protein is not annotated, while the first has a GO process annotation “Cytoskeleton organization and biogenesis”. Interestingly, more recent experimental evidence supports the hypothesis of its involvement with RNA splicing . It is rWe have described here a tool for the analysis of protein interaction maps, able to correlate the co-occurrence of sequence motifs, common GO annotations and similar Pfam domains with interactions sharing a common partner. Furthermore, we have investigated the relationship between the presence of common motifs and the presence of shared functional, component and domain data. The information given by the presence of common motifs is coherent and complementary to that present in other data sources. All these sources could be integrated to generate a large high-confidence yeast PPI dataset.As further developments of the server we are extending the approach to the search for discontinuous motifs brought together by the three-dimensional structures of the interacting proteins.We analysed yeast (Saccharomyces Cerevisiae) PPI maps obtained from different datasets: the databases BIND  and BioGThe BIND dataset contains 8.847 yeast interactions, manually curated and annotated. BioGRID contains 51.086 interactions extracted from the literature and obtained with different experimental methods. The datasets from Gavin and Krogan are from three different tandem affinity purification (TAP) experiments and contain respectively 3.500 (Gavin02), 19.973 (Gavin06) and 6.699 (Krogan) interactions. The Uetz dataset contains 942 interactions derived from a Yeast 2 Hybrid experiment.th 2006).Protein sequences were downloaded from the Ensembl Genome Browser website on July 7To assign the GO terms, we used the annotation files downloaded from the GO website and only used IDA (Inferred from Direct Assay), IGI (Inferred from Genetic Interaction), IMP (Inferred from Mutant Phenotype) and TAS (Traceable Author Statement) annotations as to avoid low-confidence or indirect annotations.The high-confidence PPI dataset (MOVIN_1) was generated by merging all the interactions that, at least in one dataset, were reported to have a motif P value smaller than 10e-4. Such dataset contains 17.733 interactions and can be downloaded from A second high-confidence PPI dataset (MOVIN_2) was generated by merging all the interactions that, at least in one dataset, were reported to have a significant sequence motif and at least a GO function or a GO component P value smaller than 10e-4. This dataset contains 14.103 interactions and can be downloaded from The authors declare that they have no competing interests.AT conceived the study, participated in its design and coordination and helped to draft the manuscript. GB helped in data analysis. PM developed the method, implemented it and performed the statistical analysis. All authors read and approved the final manuscriptList of all interactions between proteins interacting with the same partner and containing a motif with a P value <10e-4. (CSV file).Click here for fileList of all the interactions between proteins interacting with the same partner, containing a motif with a P value <10e-4 and sharing a process or component GO term with a P value <10e-4. (CSV file).Click here for file


We have conducted a protein interaction study of components within a specific sub-compartment of a eukaryotic flagellum. The trypanosome flagellum contains a para-crystalline extra-axonemal structure termed the paraflagellar rod (PFR) with around forty identified components. We have used a Gateway cloning approach coupled with yeast two-hybrid, RNAi and 2D DiGE to define a protein-protein interaction network taking place in this structure. We define two clusters of interactions; the first being characterised by two proteins with a shared domain which is not sufficient for maintaining the interaction. The other cohort is populated by eight proteins, a number of which possess a PFR domain and sub-populations of this network exhibit dependency relationships. Finally, we provide clues as to the structural organisation of the PFR at the molecular level. This multi-strand approach shows that protein interactome data can be generated for insoluble protein complexes. Trypanosoma brucei, correct generation and function of the flagellum is essential for the survival of the mammalian bloodstream stage T. brucei has a complex subdomain organisation which includes proximal, intermediate, and distal domains as well as links to specific doublets of the axoneme and the flagellum attachment zone (FAZ) Trypanosomatid protozoan parasites are the causative agents of a number of diseases responsible for the death of thousands of people in developing countries. There is currently little hope for a vaccine and existing treatment regimes are associated with high toxicity. All trypanosomes produce a single flagellum which is involved in numerous aspects of parasite biology including motility, cytokinesis, environment sensing, attachment to the host T. brucei flagellar proteome The eukaryotic flagellum is a widely conserved organelle and was a feature of the ancestor of all eukaryotes. The flagellum is implicated in an ever growing spectrum of human genetic disease T. brucei is the ability to inducibly ablate protein expression by RNAi A variety of functional genomics tools that have the potential to complement these large-scale proteomic assays of the flagellum are available. Yeast two-hybrid analyses have been extensively used to systematically investigate interactions of proteins in many model organisms Our PFR proteome forms a discreet subset of proteins within the flagellum and here we have used this as a training set for interrogating protein interactions and dependencies within the insoluble structural fraction of the flagellum. We have generated an open reading frame (ORF) library of 28 PFR proteins which is fully compatible with Gateway® cloning technology. We have used this library to perform a yeast two-hybrid screen and have identified novel protein-protein interactions within the PFR cohort. We have created a Gateway® compatible inducible RNAi vector based on p2T7-177 Trypanosoma brucei brucei were cultured at 28°C in SDM 79 medium supplemented with 10% v/v foetal calf serum (Gibco) −1.Procyclic form 5′-CTAGTGGGGACAACTTTGTACAAAAAAGTTGGCtcttgaggttccaggcttctcgagCCCAACTTTCTTGTACAAAGTTGTCCCCTTCGA-3′, containing AttB1, AttB2 and restriction enzyme recognition sites for XbaI, BamHI, HindIII and XhoI was inserted into p2T7-177 between the restriction sites for XbaI and ClaI such that the sites were destroyed. A BP reaction (Invitrogen) between this plasmid and pDNR223 was performed to obtain p2T7-177-GTW.The nucleotide sequence T. brucei (strain 927) genomic DNA by PCR using primers containing AttB1 and AttB2 recognition sequences . Extracted cells were fixed in methanol at −20°C and then labeled with BB2 Cells were settled onto glass slides and extracted by the addition of 1% Nonidet P-40 in PEME . Cells were collected by centrifugation and fixed again in 4% (w/v) formaldehyde, 2% (w/v) glutaraldehyde in 100 mM sodium phosphate buffer, pH 7.0 before post-fixation with 1% (w/v) osmium tetroxide in 100 mM sodium phosphate buffer. After several washes, cells were en bloc stained with 1% (w/v) aqueous uranyl acetate before dehydration and embedding in epoxy resin. Sections were stained with aqueous uranyl acetate and Renyold's lead citrate, and viewed in an FEI Tecnai-F12 electron microscope operating at 80 kV.Yeast two-hybrid plasmids (pDB-ORF and pAD-ORF) were transformed in yeast cells (MaV103 (MATa) and MaV203 (MATα) respectively) and selected on medium lacking the leucine or tryptophan amino acid respectively. Auto-activation of each transformed MaV103 strain was tested on four selective media We screened a number of PFR proteins identified in our comparative proteomics analysis To identify protein-protein interactions in the PFR, we designed a matrix yeast two-hybrid assay by mating strains containing each bait to strains containing each prey and subsequently testing all 700 resulting pair-wise combinations (after removing auto-activators which are discussed later). Interactions were visualised using the one colorimetric and three auxotrophic assays described previously Importantly, each of the bait constructs has been tested for self-activation in the four yeast two-hybrid assays . PFC2, PccdB toxic gene and the chloramphenicol resistance gene flanked by two attR recognition sequences necessary for LR recombination between the two tetracycline operators of p2T7-177 (T. brucei procyclic form cell line (snl4) in which inducible RNAi is targeted against PFR2. We compared the phenotype of snl4 with the previously described snl2 PFR2 knock down mutant snl2The availability of our PFR ORFeome in a Gateway® compatible format provides the opportunity to perform relatively high-throughput studies of PFR component function using genomics tools such as RNA interference. To facilitate this analysis, we modified the p2T7-177 inducible RNAi vector p2T7-177 . We nameThe interaction observed between PFC4 and PFC16 is particularly intriguing as we have previously shown that these otherwise dissimilar proteins share a small motif of 21 amino acids We then asked whether ablation of one component had an effect on the other. We generated a cell line with a Ty epitope tagged copy of PFC4 In order to determine possible post-translational modifications of PFC4 and PFC16, we analysed the protein sequences using NetPhos We have recently demonstrated the power of combining RNAi ablation of key proteins with comparative proteomics techniques to identify dependent sub-groups of proteins within the larger complex of the PFR Using immunofluorescence light microscopy of detergent extracted cells, we analysed cell lines containing a Ty epitope tagged copy of PFC3 at one of the endogenous alleles (18) and inducible RNAi against PFC3, PAR1 or PFC5 with the BB2 antibody. Comparison of cells 72 hours after RNAi induction with non-induced controls showed no gross morphological changes . In addiT. brucei benefit from the availability of a completed and well annotated genome Studies in T. bruceiHistorically, large scale forward genetic analyses have met with limited success in T. brucei that is fully compatible with the Gateway® technology. We have demonstrated the utility of this vector by ablating five distinct PFR proteins including PFR2 and in this last case have reproduced a phenotype consistent with the well characterised snl2 mutant cell line. Taken together, these tools constitute a first step towards a high throughput functional genomics system in T. brucei that we have used here in conjunction with yeast two-hybrid and proteomics techniques to generate a protein-protein interaction network for the paraflagellar rod.We have developed a new vector utilising the well characterised RNAi reverse genetics techniques available for We have identified eight novel protein-protein interactions within the PFR using a yeast two hybrid screen of 28 PFR components. Our subsequent reverse genetics analysis of the protein set provided supporting evidence for a number of these interactions and also identified several novel protein dependencies not detected in our initial screen. It is perhaps surprising that, given the central role of PFR1 and PFR2 in the PFR, no interactions were detected between these two proteins and any of the others in this study. Yeast two-hybrid analyses typically suffer from a high frequency of false negative results and this may be a particular problem when dealing with very insoluble proteins such as PFR components. There is also the distinct possibility that many interactions within the PFR do not occur on a one-to-one basis. It may be that PFR1 and PFR2 must be part of a complex in order to interact with other PFR proteins or that less abundant components interact with PFR1 or PFR2 only as cohorts. Another possibility is that a chaperone-assisted folding process that is not present in yeast is required for some PFR component interactions. Finally, there is also the very likely possibility that we have not yet identified all of the PFR components and may be missing key mediators between PFR1/2 and the other PFR proteins. All of the interactions and dependencies that we identified fall into two clusters. The smaller cluster involves two proteins that share a motif, and the larger cluster involves eight proteins  of which several contain a PFR domain.The smaller cluster consists of PFC4 and PFC16 which show consistent interactions with each other in all yeast two-hybrid assays in both bait and prey configurations. We previously showed that PFC4 and PFC16 share a 21 amino acid motif The larger cluster has two main hub interacting proteins, PFC3 and PFR5 which interact directly with each other. PFR5 interacts with PFC20 and possibly also with PFC6, although the evidence for this second interaction is weaker than that observed for other interactions in this screen. PFC6 interacts with itself and may also interact with PFR6, although again, the evidence for this latter interaction is relatively weak. It is interesting to note that PFC6 may interact with two proteins  that contain a PFR domain as well as interacting with itself. Similarly, the hub protein PFC3 interacts with two PFR domain containing proteins  as well as interacting with itself. We went on to investigate a number of these interactions by RNAi/DiGE analysis which has the power to provide context to the yeast two-hybrid data. While yeast two-hybrid analysis showed an interaction between PFC3 and PAR1, RNAi/DiGE provided an insight into the nature of this interaction in that PFC3 and PAR1 are mutually dependent on each other for correct assembly into the PFR structure. RNAi/DiGE analyses also reveal dependency relationships not detected in the yeast two-hybrid screen with incorporation of both PFC5 and PFC17 being dependent upon the presence of PFC3/PAR1. RNAi ablation of PFC5 does not affect the assembly of PFC3, PAR1 or PFC17 which suggests a hierarchical directionality to this dependency network.One surprising, and likely significant result from our analysis is that a successful ablation by RNAi of the PFC3/PAR1 network results in no gross changes in the morphology of the PFR polymeric structure. This may represent evidence that the three major zones of the PFR and their distinct morphologies are heavily reliant for their formation on only a relatively few structural proteins, perhaps PFR1 and PFR2 . A picture then emerges of these few structural proteins being responsible for the major PFR lattice structure onto which is built a cohort of numerous other proteins with domain architectures suggesting a role in metabolism, signalling and regulation T. brucei and have used these to undertake the first large protein-protein interaction study of the PFR. Using a combination of technologies, we have identified eight novel protein-protein interactions and five protein dependencies. By combining the information presented in this study with the interactions and dependencies previously identified, we can summarise the current state of our knowledge of the PFR protein network , with PFR5 and PFR6 (both in one assay).(0.29 MB PDF)Click here for additional data file.Figure S2Auto-activation assay of PFR baits. A. Cartoon illustrating that some ORFs fused with the Gal4 DNA binding domain have the ability to activate the transcription of the reporter genes.This behaviour has to be tested to decrease the false positive rate. B. The diagram shows the position of each DB-ORF for this assay. C. Readout of the four autoactivation assays Click here for additional data file.Table S1PCR primers used to generate the ORFeome. AttB1 and AttB2 recognition sequences are shown in lower case.(0.01 MB PDF)Click here for additional data file.


Is it possible to identify what the best solution of a docking program is? The usual answer to this question is the highest score solution, but interactions between proteins are dynamic processes, and many times the interaction regions are wide enough to permit protein-protein interactions with different orientations and/or interaction energies. In some cases, as in a multimeric protein complex, several interaction regions are possible among the monomers. These dynamic processes involve interactions with surface displacements between the proteins to finally achieve the functional configuration of the protein complex. Consequently, there is not a static and single solution for the interaction between proteins, but there are several important configurations that also have to be analyzed.DockAnalyse. This application is based on the already existing DBscan clustering method, which searches for continuities among the clusters generated by the docking output data representation. The DBscan clustering method is very robust and, moreover, solves some of the inconsistency problems of the classical clustering methods like, for example, the treatment of outliers and the dependence of the previously defined number of clusters.To extract those representative solutions from the docking output datafile, we have developed an unsupervised and automatic clustering application, named DockAnalyse makes the interpretation of the docking solutions through graphical and visual representations easier by guiding the user to find the representative solutions. We have applied our new approach to analyze several protein interactions and model the dynamic protein interaction behavior of a protein complex. DockAnalyse might also be used to describe interaction regions between proteins and, therefore, guide future flexible dockings. The application (implemented in the R package) is accessible. Protein-protein interaction (PPI) is the key process by which most of the proteins fulfill their function, and interactomics represents one of the current frontiers of biosciences ,2. It ishttp://www.rcsb.org/pdb/home/home.do; [Nuclear Magnetic Resonance (NMR) and X-Ray Crystallography (XRC) are the two main technologies applied for structure elucidation, but these hi-tech methods are frequently constrained by the methodological requirements when dealing with protein complexes. It is assumed that these experimental limitations have reduced the amount of large protein complexes solved and, therefore, protein complexes have become less represented in the structural databases such as the Protein Data Bank . Therefhome.do; ,14. In rUsually, it is considered that the best solution given by a docking program is the one with the best interaction energy, but quite a lot of the real interactions tend to involve large surface displacements with non-optimal interaction energies to finally form the protein complex. These displacements occur along the protein surface, generating multiple low-energy interaction complexes. In these cases, these low-energy interaction regions might not be, in reality, less important from a functional point of view, and the interaction region has to be wide enough to allow PPIs coming from different orientations like, for instance, proteins that require movements among them when they act as a protein complex. Owing to all of these facts, interaction among proteins seems to be a dynamic mechanism where there is not only one single solution with the best interaction energy, like most of the current PPD programs consider, but rather there are several solutions with more or less interaction energy, and not necessarily does the native form have the best theoretical solution .DockAnalyse, which is based on the already-existing DBscan clustering method [DockAnalyse guides the search for good docking candidates by reducing the huge amount of putative docking solutions to check. Furthermore, the use of DockAnalyse allows a global vision of many characteristics of the PPI process through different data, graphical representations and possible personalized searches which also guides the search for signifiant solutions.Our approach attempts to deal with these particularities by considering the global contribution of the PPD calculated solutions and selecting those representative solutions (docking solutions that are centers of clusters and have high interaction energy) that describe a general behavior of a subset of solutions without improving an unrealistic one. To extract those solutions that best describe the real dynamic mechanism of interaction from the output datafile of the current PPD programs, we have developed an application, called g method  and, morDockAnalyse may help us to theoretically postulate the structure of the studied protein complex or to propose the way by which certain proteins interact together in a mobile fashion to execute a biological function. Moreover, the analysis made by DockAnalyse might guide future flexible PPD approaches, because of the important PPD information obtained from the use of this new application.The exhaustive analysis of all of the PPI structures obtained with With the aim of elucidating which of the docked structures between two studied protein structures are the most important from a functional point of view, an unsupervised procedure, based on the already existing DBscan clustering method , was desA =  and B =  are the angles corresponding to two docking solutions, then the distance between A and B is defined as:First, distances among angles of the different docking solutions are computed. For instance, assume that i d= min{|i b- ia|, |i b- i a+ 2π|, |i b- i a= 2π|} for i = 1, 2, 3.where i dis the angular distance between angles i aand ib. Once the distance matrix between angles of docking solutions has been computed, Multidimensional Scaling (MDS) [That is, ng (MDS)  is perfong (MDS)  for moreDockAnalyse, was applied to interpret the results obtained from different PPD assays because it gives a lot of information that is produced by the PPD output datafile analysis. As well as this information, the shape, size and distribution of the clusters obtained along with the position of the outliers are shown in DockAnalyse result graphical representations  to approximately less than 10 in most of the cases. Some PPD programs do not incorporate a clustering process and the use of DockAnalyse in these cases is even more justified. Evidently, DockAnalyse gives researchers the possibility to use it with a greater or lesser number of docking solutions although this characteristic has been proposed to guarantee the exhaustive exploration of the whole space of docking solutions.DockAnalyse, when trying to interpret the results from a docking procedure, is shown in Figure DockAnalyse is much more intuitive than from the raw numerical data given by most of the PPD programs.The main advantage of DockAnalyse can be applied in a systematic way to monitor the quality and type of docking predictions. This group of known protein complexes included homodimers and heterodimers, protein inhibitors and other enzyme complexes. The unbound structures of the interacting proteins were used when available; otherwise, the bound structures were extracted from the complex.Through a set of 35 Enzyme/Inhibitor or Enzyme/Substrate protein complexes of the current Protein-Protein Docking Benchmark 3.0  . In comparison to the crystallographic protein complex structure, which was obtained from the benchmark set, all of these satisfactory solutions showed a very low RMS (Root Mean Square) deviation. This means that only through DockAnalyse outputs could it be seen in these cases that the dockings were credible before realizing that the RMS deviation was so low. On the contrary, the percentage of unsatisfactory dockings detected was 28.57%, where an unsatisfactory docking means that all of the clusters given by DockAnalyse are composed of few members and, moreover, have very low interaction energies. The RMS deviation values calculated here were all very high. Again, with DockAnalyse these unsatisfactory dockings could be detected before knowing their high RMS deviation values. RMS deviation values could be calculated because we knew the crystallographic structure of the protein complex from the benchmark, but in real research it will almost never be known, so DockAnalyse might be used at this point to guide the researcher concerning the quality and credibility of the docking. In addition, 17.14% of the dockings were considered to be static interactions with a RMS deviation again very low in the considered solutions. The way by which DockAnalyse can detect this type of static interactions is explained below.The percentage of satisfactory dockings detected was 51.43%, where a satisfactory docking is the one on which one or more DockAnalyse as a clear trajectory. These values do not belong to any cluster so, consequently, they are included initially in Cluster 0, but the graphical representations provided by this tool and the different information given could help the user to realize that he is being faced with two proteins with a small binding site and without any permitted flexibility.These interactions could be detected with our program due to the possibility to perform personalized searches, introducing specific PPD solution values in the "marker" variable of the program source code (See the "readme" file of the program). In Figure DockAnalyse application. In Figure DockAnalyse results, but in the PPD program it is ranked as Solution 22, far from the optimal solution although both RMS deviation and energy values are significant. It has to be considered that DockAnalyse highlights this solution as one of the most representative because it is at the center of the cluster with the highest interaction energy (Cluster 2). Moreover, as can also be seen in Figure DockAnalyse.As has been reported thoroughly in previous sections, the main potential of this new method is the capability to explore the interaction space, making clusters that correspond to extensive contact regions. These graphical representations reproduce the movements that occur between the constituents of a protein complex. These preassumed non-optimal solutions described before have lower scores in the PPD program outputfile, therefore, they are discarded by the PPD initial filter. As shown below in the example, these solutions could be rescued and their score improved with 1BVN) of the benchmark, DockAnalyse outputs showed a satisfactory docking in which Cluster 14 was significant. Using the supplementary scripts that come with DockAnalyse, all of the ligand positions of the solutions of Cluster 14 were extracted as PDB files and then loaded in a protein modeling and visualization tool with the structure of the receptor. As can be seen in Figure DockAnalyse. Furthermore, that is another useful way to apply our program.For Protein Complex 3 :An example of a procedure where DockAnalyse was applied with the aim of reducing the huge amount of docking solutions obtained to several representative ones. These solutions were the 4th, 78th, 28th, and 1st initially ranked solutions of the Escher NG docking output datafile. Here, the main utility of DockAnalyse in reducing the number of solutions to analyze after a PPD calculation is shown. For these four representative docking solutions, the protein structure (PDB) files were obtained, merged into a trajectory file and then subsequently loaded into a protein modeling and visualization tool with which we could analyze them. This procedure allowed us to build a point-to-point pseudo-trajectory with which we could postulate a model to explain the surface displacements between the given proteins  prosthetic groups ,21. ThesA comprehensive tool for the analysis of PPIs has been designed. This new application permits a better interpretation of the obtained PPD solutions as well as the surface displacements that may occur during the interaction between the proteins of a protein complex. Therefore, this tool guides the modeling of a protein complex and can be applied in a systematic way to monitor the quality and type of docking predictions through global or local visions of the docking results that facilitate the desicion making process regarding the docking characteristics. The simplicity in applying this tool and the ease in interpreting the PPD solutions makes it ideally suited to analyze the data obtained in a PPD experiment. Considering all of the facts stated above, to go further and propose new functional interpretations for the proteins of interest might be much easier. In terms of these new hypotheses, when the initially docked proteins are monomers, a proposal on the putative structure of a multimeric protein complex might be postulated ,4. AnothDockAnalyse could be used after a docking assay in the context of a more complex procedure where a model of the behavior between the proteins that take part in a biologically functional protein complex would be performed. A schematic description of how to use DockAnalyse in this whole bioinformatics procedure is shown in Figure DockAnalyse would be. After that, the newly developed algorithm has to be applied to each of the docking output datafiles to obtain the representative docking solutions among those thousands calculated. Lastly, manual curation of the obtained docking representatives might be necessary to fit the solutions given with the appropriate biological function and to eliminate the putative aberrant results. The combination of theoretical docking procedures with the available experimental information is shown to greatly improve the modeling. DockAnalyse is accessible at: http://bioinf.uab.es/rker/DockAnalyse/DockAnalyse.zip  which are separated by regions of low density (considered as outliers). The lack of an appropriate outlier is a well-known weakness of one of the classical clustering methods like k-means, where even very far points from the closest centroid are included in the same cluster without any additional criteria. This clustering method was chosen because it is extremely robust and it also solves some inconsistency problems that usually appear when applying other clustering methods. In general, the classical clustering methods do not manage the outliers well, while DBscan tends to treat these isolated points much better and it allows for the finding of all cluster members independently of the cluster shape, discarding the outliers. One of the main problems of clustering is that the classical methods are dependent on the previously defined number of clusters, while DBscan is not.DockAnalyse we also used it to sort these clusters taking into account the number of members of each cluster. The more members a cluster has the better the solution is. This property allows for the sorting of the solution according to the number of members/solutions per cluster, and at the same time it allows for the removing of non-relevant docking solutions, which is the objective of fixing our k = 15 (minimum number of points in a cluster). The best solutions, which are the only ones that will be checked by the user of DockAnalyse, are integrated by hundreds of points. Those clusters with less than 15 members are discarded, therefore, they have no effect on the final result. This is done because nobody is going to check a docking representative with such a limited number of docking solutions supporting the goodness of this region to be a binding surface. Besides, the user can always access the script source code to modify the k parameter (minimum number of points in a cluster), but this is only an optional possibility because, as previously commented, this value has no relevant effect on the final results.The DBscan algorithm depends on two tuning parameters that define the density connection . Ester et al. (1996)  indicateDockAnalyse is currently designed to be used with whatever version of Escher NG or Hex [DockAnalyse results would be.G or Hex -26 PPD pDockAnalyse and its use, performed the analysis, and wrote the manuscript. JC designed the use of the new tool, participated in the analysis and drafted the manuscript. PD and JC developed the new application in R. EQ and JC conceived of the study, participated in its coordination and helped to draft the manuscript. AG helped to analyze some data and drafted the manuscript. SB tested the new application. All authors read and approved the final manuscript.IA participated in the design of This research was supported by Grants  from the MCYT , from the Centre de Referència de R+D de Biotecnologia de la Generalitat de Catalunya and from Fundació La Marató de TV3. EQ is Chair-Holder of a “Chair of Knowledge and Technology Transfer Parc de Recerca UAB- Santander”. AG acknowledges a postdoctoral fellowship from “Parc de Recerca UAB- Santander”. IA is a predoctoral fellowship recipient from the UAB (Universitat Autònoma de Barcelona). The English of this manuscript has been corrected by Mr. Chuck Simmons, a native English-speaking Instructor of English of this University.DockAnalyse. 4 R program files, 4 Perl scripts, 2 text files and 3 zippped libraries.Click here for file