Currently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is.In the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built – the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction.The clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported.Although the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes.  Some li1. Whether two proteins prefer to interact?2. If more than two proteins purportedly interact with the same protein, can they interact concurrently by binding two separate regions of the protein, or does one exclude the other because their binding sites substantially overlap?3. What are the relative binding strengths of proteins within a cluster?http://www.thebiogrid.org) [et al. [We choose the yeast protein-protein interaction network from the online database BIOGRID(rid.org) . The num [et al. . The curWe applied an eigenmode analysis to cluster the protein interaction network. We formed the Kirchhoff matrix  M; the iThen, we performed eigenmode analysis of this matrix M. This definition automatically leads to a singular matrix (i.e. the determinant of the matrix M is zero) that must be analyzed with Singular Value Decomposition .We calculated all eigenvalues and eigenvectors of the connectivity matrix by applying the SVD subroutine available in the LAPACK library .If A is any matrix of size m×n (with m>=n), then A can be written as a product of three matrices:T						(2)A = UΛV1, λ2 , … ,λn along the diagonal and zeros off diagonal, and U and V are two matrices of sizes m×n and n×n, respectively, having orthogonal columns, i.e.where Λ is the square matrix of size n×n containing nonnegative values λandThe Kirchhoff matrix M can be written asT      						    	(4)M = VΛU1, λ2 , … ,λn of M and U is the matrix formed from eigenvectors of M. Thus, the elements Mij of the contact matrix M can be expressed aswhere Λ is the diagonal matrix containing eigenvalues λki denotes the ith component of the eigenvector corresponding to the kth eigenvalue. Equation 4 is the eigenvalue expansion of the contact matrix. From Equation 5, it follows:where uThe eigenvalues with the smallest indices corresponding to the largest absolute values of λ make the largest contributions and smaller eigenvalues contribute successively less .For each eigenvalue there is a corresponding eigenvector. The significant components of an eigenvector comprise a cluster where each component corresponds to one protein. The components with an absolute value greater than 0.05 are assumed to be significant . The cluhttp://www.rcsb.org), or if there is no structure of the protein we predict the structure by comparative modeling. Figure After the clusters are constructed, we need to choose a cluster to do structural analysis. Figure http://zhanglab.ccmb.med.umich.edu/I-TASSER/), which gave the best protein models at the Critical Assessment of Structure Prediction (CASP 7 and CASP 8), a community-wide, worldwide experiment designed to obtain an objective assessment of the state-of-the-art in structure prediction [To predict an interaction complex or predict a new interaction, we require the protein structures of both interacting proteins. If the structure of a protein is not available in the PDB, we use comparative modeling approaches ,12. To pediction -17. The  The I-TASSER server returns the best five models with a c-score attached for each model. Also it returns the top ten templates used in the threading. The c-score is a confidence score that I-TASSER uses to estimate the quality of the predicted model. The calculation of c-score is based on the significance of the threading template alignments and the convergence parameters of the structure assembly simulations. When selecting one of these models, we select the model that comes from the largest cluster and has the best c-score. C-score is in the range ,2, wherehttp://cluspro.bu.edu/) returns the top models based on energy and cluster size. We select one of the returned models after considering the energy and the size of the cluster – preferring lower energies and larger cluster sizes. As the Cluspro server implements rigid body docking, when a partner protein in a complex is structurally flexible Cluspro is not so able to account for this flexibility.After we have both structures in an interacting pair we use docking to predict the protein complex formed in a protein-protein interaction. We use the Cluspro server -23 for dWe perform the eigen-analysis on the yeast network version 2.0.40  and 2.0.41  and find that the number of zero eigenvalues are 6 and 3, respectively, which are very small compared to those from the yeast network Sen et al. previously used [The proteins and their interactions in clusters 10, 14, and 15 are shown in Figure http://funspec.med.utoronto.ca/) [We search the gene ontology database  for the nto.ca/) , a web bnto.ca/) . For eacwhere, G = the size of the genome; C = the number of genes in the genome having that attribute; n = the size of the query cluster; k = the number of genes in the cluster known to have that attribute .-3) for the three clusters we are reporting here. These small p-values signify the relatively strong functional coherency of these clusters. How small must a p-value be in order for a cluster to be functionally coherent? FunSpec uses 0.01 as a cut off, which is arbitrary. For each of the clusters, we obtain p-values that are much smaller than 0.01, indicating the highly probable functional coherency of the clusters.Most of the p-values in Table One of our goals in this paper is to test the validity of a reported interaction by using structural information about the interacting proteins in a cluster. Our idea is simple: first, find the structures of the two interacting proteins from the PDB . If the 2 and 15,074Å2, respectively which are similar. Also the surface areas of the model of YDL020C and its template (PDB id 1z1nx) are 33,655Å2 and 33,482Å2, respectively. The similarity in these surface areas can serve as a crude indication of the quality of the model returned from the server. In cluster 14, there are nine interactions. Four interactions involve YML032 whose model returned from the I-TASSER server is not a globular protein. This model is a very extended open structure. As a result, it would appear to have significant structural flexibility and thus not be fully suitable for rigid body docking using Cluspro. We have performed docking for the other five interactions. Results of docking for these five interactions are shown in Figure Here, we show an example of this approach. We find the homologs for the six proteins in cluster 14 shown in Figure YOL001W has 100% sequence identity with 2PK9 chain B and the template used by I-TASSER to predict the structure of YBR160W is 2PK9 chain A. This suggests that there might be an interaction between YOL001W and YBR160W because of this known dimeric structure. We docked the homolog (2PK9 chain A) of YOL001W and the model for YBR160W. The docked complex, YBR160W.YOL001W, is shown in Figure It is evident from Figure It is evident from the model for YML032C in Figure We have used the ratio between the buried surface area and the total surface area of a protein complex as a measure for the strength of an interaction. Although we cannot definitely say whether an interaction actually happens or not from the value of this ratio, the value itself gives a certain level of confidence in that interaction.This work has taken the approach of predicting new protein-protein interaction complexes and their interactions  through docking of their molecular structures. Since not all complexes are available in the PDB, nor are they all likely to ever be available, we have relied upon comparative modeling and docking methods. Their recent improved reliability gives some justification for the use of these approaches. This methodology has the advantage that it can also identify interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. However, because of the lengthy computational times and the required human judgment to select models from the results of the prediction programs for comparative modeling and docking, this process cannot yet be fully automated. Nonetheless many such cases can be investigated, and it appears that the results can provide important new information.In this computational prediction of interaction complexes, new interactions, and concurrency or exclusiveness of multiple interactions require two major computational steps – comparative modeling (I-TASSER server ) and docValidation of these predictions is an important task. At this time, we have not experimentally validated these predictions of new protein-protein interactions and their complexes. Because of the relatively few structures for protein complexes in the PDB database, we have not found clusters where the structures for the predicted complexes are available in the PDB database. Therefore, at this point, the correctness of our results depends on that of the underlying computational methods – techniques for comparative modeling, clustering, and buried surface area computations. Our theoretical predictions might be however useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes.The authors declare that they have no competing interests.ARK, AK, and RLJ all contributed to the design, execution and writing of this work.


Assuming a negative-to-positive ratio of 600∶1, the final classifier achieves 89.3% coverage at an estimated false discovery rate of 10%. Finally, we applied our prediction method to two recently described APMS datasets. We find that our predicted positives are highly enriched with CCPPs that are identified by both datasets, suggesting that our method successfully identifies true CCPPs. An SVM classifier trained from heterogeneous data sources provides accurate predictions of CCPPs in yeast. This computational method thereby provides an inexpensive method for identifying protein complexes that extends and complements high-throughput experimental data.Proteins do not carry out their functions alone. Instead, they often act by participating in macromolecular complexes and play different functional roles depending on the other members of the complex. It is therefore interesting to identify co-complex relationships. Although protein complexes can be identified in a high-throughput manner by experimental technologies such as affinity purification coupled with mass spectrometry (APMS), these large-scale datasets often suffer from high false positive and false negative rates. Here, we present a computational method that predicts co-complexed protein pair (CCPP) relationships using kernel methods from heterogeneous data sources. We show that a diffusion kernel based on random walks on the full network topology yields good performance in predicting CCPPs from protein interaction networks. In the setting of direct ranking, a diffusion kernel performs much better than the mutual clustering coefficient. In the setting of SVM classifiers, a diffusion kernel performs much better than a linear kernel. We also show that combination of complementary information improves the performance of our CCPP recognizer. A summation of three diffusion kernels based on two-hybrid, APMS, and genetic interaction networks and three sequence kernels achieves better performance than the sequence kernels or diffusion kernels alone. Inclusion of additional features achieves a still better ROC Many proteins perform their jobs as part of multi-protein units called complexes, and several technologies exist to identify these complexes and their components with varying precision and throughput. In this work, we describe and apply a computational framework for combining a variety of experimental data to identify pairs of yeast proteins that partipicate in a complex—so-called co-complexed protein pairs (CCPPs). The method uses machine learning to generalize from well-characterized CCPPs, making predictions of novel CCPPs on the basis of sequence similarity, tandem affinity mass spectrometry data, yeast two-hybrid data, genetic interactions, microarray expression data, ChIP-chip assays, and colocalization by fluorescence microscopy. The resulting model accurately summarizes this heterogeneous body of data: in a cross-validated test, the model achieves an estimated coverage of 89% at a false discovery rate of 10%. The final collection of predicted CCPPs is available as a public resource. These predictions, as well as the general methodology described here, provide a valuable summary of diverse yeast interaction data and generate quantitative, testable hypotheses about novel CCPPs. Proteins carry out most of the work in the cell, and they frequently do so by interacting with other proteins. Therefore, understanding protein and hence cellular function often entails knowing about various types of protein-protein interactions. This paper describes a method for predicting these interactions using a supervised classification algorithm that learns from a variety of genome-wide data sets.Three classes of methods for predicting protein-protein interactions are described in the scientific literature. The first class consists of docking methods that employ detailed molecular simulations to dock two protein structures. These methods do not scale to the entire genome, both because they require protein structures and because they are computationally expensive. High-throughput computational methods fall into two classes: those that predict direct physical interactions kernel methods, that make use of a specific notion of pairwise similarity (kernel functions) to project data into a high-dimensional vector space. The benefits of the kernel approach are three-fold: the kernel function can incorporate prior knowledge of the problem domain; the kernel function can operate on non-vector data such as strings, sets or graphs, and kernel algebra allows us to combine heterogeneous types of data within a single classification framework. The SVM algorithm and its application to biological data is described in an accessible fashion in We frame the problem as a supervised learning problem, and we train a support vector machine (SVM) classifier to discriminate between pairs of proteins that are co-complexed and pairs that are not. The SVM is a non-parametric statistical method for discriminating between two classes of data. SVMs have been applied widely in bioinformatics, in applications as diverse as protein homology detection, alternative splicing prediction, microarray analysis and mass spectrometry analysis The ability to learn from heterogeneous data is of particular value in the prediction of CCPPs, because so many types of data are relevant to this task. In this work, we define separate kernels that operate on each relevant data type. These include three kernels on protein sequences, three kernels on different types of protein networks derived from high-throughput data, and kernels on gene expression, interologs, Gene Ontology terms, co-regulation and localization data. We combine all of these kernels in a single classifier that achieves state-of-the-art predictive accuracy.diffusion kernelIn this work, we demonstrate the utility of a particular type of kernel, the The resulting protein interaction networks have been shown to exhibit several distinctive properties These network properties can be exploited to improve statistical inferences about protein-protein interactions. Tong et al. MCC considers the number of common neighbors shared by two vertices, i.e., it only considers paths of length two. In this study, we generalize upon MCC by using the diffusion kernel, which takes into account paths of all lengths 50 score from 0.859 to 0.937.Next, we show that integration of different data sources improves the performance of our classifier. The summation of sequence and diffusion kernels yields much better performance than either sequence or diffusion kernel alone. This classifier successfully identifies 4789 out of 10980 positives before producing the first false positive. The addition of features such as co-expression, co-regulation, interolog, co-localization and GO annotation improve the ROCWe also validate our method using two recently decribed large scale APMS data sets http://www.yeastrc.org/pdr) After validating our method, we trained two SVM classifiers, one using all available data, and one that excludes GO annotations, and applied both classifiers to all pairs of yeast proteins. The resulting predictions are available through the Yeast Resource Center  at random from among all protein pairs that do not co-occur in any top level MIPS complex http://noble.gs.washington.edu/proj/coco.The complete collection of labeled examples, as well as all of the kernels described in the next section, are available at the on-line supplement iX,jX> can be replaced with a generalized similarity function K, known as the kernel function. If the kernel function is positive semidefinite and symmetric, then there provably exists some vector space (the feature space) in which the kernel function plays the role of the scalar product. In other words, if Φ defines a mapping from the space that the data resides in (the data space) into the feature space, then Φ(iX)×Φ(jX) = K. The kernel function provides an intuitive way to encode prior knowledge about a data set. Furthermore, kernel methods provide a natural way of combining heterogeneous data sources A kernel method is an algorithm that can be written such that all occurrences of data vectors appear within a scalar product operation. When this is the case, the scalar product operation <K that measures the similarity between two proteins, the corresponding tensor product pair kernel (TPPK) pK is defined as pK ,) = KK+KK. It is straightforward to show that the feature space of pK defined on protein pairs is equivalent to the tensor product of the feature vector spaces of K defined on individual proteins.Predicting edges in a protein interaction or co-complex network presents an additional difficulty for which kernels can provide a solution. Many relevant types of data—protein sequence, gene expression, etc.—concern individual proteins, whereas the predictor evaluates protein pairs. This begs the question, how do we define a similarity between two pairs of proteins, given a similarity function that is defined on single pairs. Several groups have used SVMs to predict protein-protein interactions RK = exp(−γ∥A–B∥2), with γ = 0.5. Finally, for networks, we use the diffusion kernel, defined as follows. Given a graph G = , define a generator matrix H:H corresponds to the adjacency matrix with the diagonal entry equal to the negative of the degree of the corresponding vertex. The diffusion kernel matrix DK is then computed as the exponential of the generator matrix: DK = eHβ. DK can be regarded as the sum of probabilities of reaching j from i following all paths from i to j in a random walk. The parameter β controls how rapidly a random walk diffuses away from a vertex. In this study, we use a fixed diffusion parameter of 1. It can be shown that the exponential of any symmetric matrix is symmetric and positive semidefinite. Therefore, the matrix DK is a kernel matrix.In this work, we employ a variety of kernel functions. Three different amino acid sequence kernels are described in the next section. For several vector data types, we use a radial basis kernel (RBF) Our CCPP predictor combines eleven different data types, listed in We include three sequence-based kernels in this study: the spectrum kernel We apply the diffusion kernel to three kinds of networks derived from the BIOGRID database For the remaining data sets, rather than define a kernel on proteins and then apply TPPK, we directly compute features of protein pairs. We then concatenate the resulting features and apply a radial basis kernel to the resulting vectors.Two proteins in the same complex are likely to exhibit correlated gene expression profiles. We derive co-expression features from five different microarray expression data sets: diauxic shift p value reported by Lee et al. Proteins in the same complex are likely to be under similar transcriptional control. The same transcriptional regulator might bind to their regulatory elements, which can be measured by ChIP-chip assays. We compute the co-regulation kernel based on the data from Lee et al. Saccharomyces cerevisiae are used to infer the CCPP relationships. All the yeast ORF sequences are searched against the non-redundant database for two iterations with an E-value threshold of 0.005 to generate a position-specific scoring matrix. The matrix is then used to score the yeast ORFs against a database consisting of proteins in non-yeast BIND complexes for one iteration with an E-value threshold of 10 to identify the homologs. The feature values based on interologs can then be computed based on the negative log E-values between sequences: h = imaxj,{Imin,l)}, where proteins i and j are sequence homologs of A and B respectively, I is an indicator function indicating that i and j are two proteins in the same complex from another organism based on BIND, and l is the negative log E-value from PSI-BLAST search between protein A and protein i.If the homologs of two proteins in another organism are in the same complex, then these two proteins are also likely in the same complex in yeast lf be the fraction of proteins present in a location l, and let Ibe an indicator function indicating that protein i is observed to localize to l. The co-localization feature values are then computed as follows: l = lmax{II(−log(lf))}; i.e., the feature value for protein pair  is computed as the negative logarithm of the fraction of proteins present in the most specific location where both proteins A and B are observed to localize.If two proteins are in the same complex, then they must be localized to the same compartment at some time. Fluorescence microscopic studies have been used to map the location of yeast proteins on a genomic scale. We derive a co-localization kernel based on the results of Huh et al. T, then we also add the annotations of all ancestors of T to the protein. Two proteins in the same complex are more likely to have similar GO term annotations. Let gf be the fraction of proteins annotated with a particular GO term g, and let I be an indicator function indicating that protein i is annotated with GO term g. The GO feature values are then computed as follows: G = gmax{II(−log(gf))}; i.e., a single feature value for protein pair  is computed as the negative logarithm of the fraction of proteins present in the most specific GO term where both proteins A and B have annotations. We derive three different features for the three ontologies.The Gene Ontology (GO) A represents the amino acid sequences, the three N's represent the three interaction networks, the five D's represent five pairwise data types, and “:” indicates vector concatenation.The eleven different kernels are combined in two stages. First, the three sequence kernels and the three diffusion kernels are individually normalized by projecting onto the unit sphere, via http://pyml.sourceforge.net). Three-fold cross-validation with C = 10 is carried out to evaluate the performance. In each split, each partition contains the same number of positive and negative data points.We use the publicly available PyML implementation of the support vector machine algorithm  curves. This curve plots number of true positives as a function of number of false positives for varying classification thresholds. Our performance metric is ROCThe ROC curve does not take into account the negative-to-positive ratio in the data set. In the application of a classifier, we are often interested in the false discovery rate, the fraction of predicted positives that are false positives. This metric is highly dependent on the negative-to-positive ratio. We report the false discovery rate of the kernel with all features assuming a negative-to-positive ratio of 600, which is the estimated ratio in the real scenario We begin by demonstrating that, when directly ranking protein pairs, the diffusion kernel improves the quality of the CCPP predictor. Goldberg and Roth Next, we show that using a supervised learning algorithm improves over direct ranking. We train a support vector machine using the TPPK of the diffusion kernel, and we compare the SVM's performance with that of the simple method of ranking pairs by the diffusion kernel values between the two vertices directly. 50 plots of SVMs trained using the same networks but two different kernels. To compute the linear kernel, each row of the adjacency matrix of a network is treated as a feature vector, and the inner products between two rows are computed as the corresponding kernel value. Like MCC, the linear kernels consider only paths of length two. We normalize the linear kernels and transform them using TPPK, as was done for diffusion kernels. Finally, we show that, in the context of SVM classification, the diffusion kernel yields better performance than the simple linear kernel. Different types of high-throughput assays yield complementary information about CCPPs. We therefore trained a single SVM using all three networks simultaneously. 50 performance from 0.859 to 0.937.Finally, we combined the diffusion kernels with the sequence kernels and with the five protein pair data sets. As shown in 50 plot. In particular, six pairs are ranked high by the final classifier but are not labeled as positives according to MIPS. We investigated each of these pairs. The proteins ARC40 and ARC35 are annotated to be in complex ARp2/3 complex by the Saccharomyces Genome Database (SGD), and NOP14 and UTP7 are annotated to be in complex U3snoRNP by SGD. Thus, these two pairs are likely true positives missed by the MIPS database. In another top-ranked pair, UTP9 is a component of the U3snoRNP that is involved in processing of pre-18S rRNA, and CBF5 is the pseudouridine synthase catalytic subunit of box H/ACA snoRNPs, which is also involved in rRNA processing. This pair has been identified by two APMS studies The accuracy of the top-ranked predictions with the inclusion of additional RBF kernel seems to decrease compared with the TPPK of sequence and diffusion kernels alone, as indicated in the leftmost region of the ROC5 = 41.0%. In our experiments, most of the data sources have a significant proportion of missing data, and the coverage varies across the data sets. A significant concern for any method that simultaneously exploits multiple types of data arises from the increased prevalence of missing data. If each given data type is missing 10% of its entries, then in a data set consisting of four such data sources the probability that a given example will have missing data from at least one source is 1−0.9We therefore investigated how much the SVM's performance depends on the availability of all the data sources used. We examined the 7,510 true positive interactions identified before the 50th false positive for the SVM trained using the summation of the three diffusion kernels. For each correctly predicted protein pair and each of the three networks, we asked whether there exists an edge between the proteins and whether there exists a path of any length between the two proteins. In the previous sections, we compared the performance of various methods using a data set with the number of negatives chosen to be the same as the number of positives. In reality, the number of negatives is much larger than that of positives, and the negative-to-positive ratio has been estimated to be around 600 We train two classifiers, one that uses GO term annotations and one that does not. GO term annotations are sometimes derived from experimental observations of physical interactions or complex memberships. Using GO term features may therefore artificially inflate the performance of the classifier. To eliminate the possibility of circularity, we trained a classifier using the TPPK kernel and the RBF kernel without GO term features. On the other hand, biologists may be interested in the best possible predictions we can get using all available data. Especially when we apply the classifier to the prediction of protein pairs without interaction data, the issue of circularity is not a concern, and GO term annotations from sources other than physical interaction or complex membership may be useful in CCPP prediction. Therefore, we also trained a classifier that includes the GO term features.Saccharomyces cerevisiaep-value of the subset containing at least 619 pairs in both APMS data sets is 4.2e–198. Because the pairs in both APMS data sets are believed to be more reliable than the rest of the pairs in the data set, it is reassuring that the positives predicted by our model are enriched in these reliable pairs. Our model predicted a larger fraction of pairs to be positive in the data set of Gavin et al. (48.5%) than in the data set of Krogan et al. (38.0%)Advances in tandem affinity purification (TAP) followed by mass spectrometry make it possible to characterize complexes on a large scale. Recently, two groups published high throughput identifications of complexes in p<1e–300). Thus, the CCPPs predicted by our classifier are consistent with the results of Collins et al.Collins et al. 50 of 0.69 with standard deviation of 0.05. This is slightly better than the best result (0.68) by Qi et al. Qi et al. published their study before the availability of the two recent large scale APMS studies 50 is 0.68 with a standard deviation of 0.05. This is similar to what Qi et al. reported as their best performance.Qi et al. Qi et al. simulates a realistic scenario by using a negative-to-positive ratio of 600∶1 in the training set. However, in their setting, each classifier only learns from 50 positive pairs. Because of this relatively small number of positives in the training set, the resulting classifier will likely not generalize as well as a method that learns from all available positive pairs. This is why we instead chose to train on a data set with all available positive training pairs and a negative-to-positive ratio of 10, and simulate the real scenario by magnifying each false positive by 60, as described above.Saccharomyces cerevisiae, excluding 809 dubious open reading frames and 7 pseudogenes. For the SVM trained without GO term annotations, 19,258 out of 17,307,786 protein pairs are identified using an FDR threshold of 10%, including 3,946 pairs that are not already annotated in the MIPS complex catalogue. http://www.yeastrc.org/pdr), and all predictions obtained using an FDR threshold of 10% are included in the browseable interface of the repository.Having demonstrated that our method produces accurate predictions, we proceeded to apply the two classifiers described previously—trained with and without GO annotations—to all protein pairs in We analyzed the novel predictions produced by the classifier trained without GO annotations. First, we divided these novel predictions into two sets: those protein pairs in which one protein is a member of a MIPS complex, and those pairs in which neither protein is in the manually curated MIPS complex catalogue. Among the 3,946 novel CCPPs, 3,260 pairs are linked to one of the 1,237 members of any MIPS complex, and the remaining 686 pairs do not involve any MIPS complex.http://noble.gs.washington.edu/proj/coco.We began by investigating the extent to which the former set of predictions extend known MIPS complexes. Ideally, a newly identified member of a protein complex would be predicted to co-complex with all known members of that complex. We therefore identified all proteins that are predicted to be co-complexed with every member of a known MIPS complex containing at least five proteins. These predictions are listed in clique is a set of nodes with every pair of nodes in the set connected by an edge, and a maximal clique is a clique to which no node in the graph can be added to create a larger clique. In general, finding maximal cliques is an NP-hard problem, but because our network is relatively small and sparse, we were able to perform exhaustive enumeration to identify all maximal cliques. We thereby identified 199 maximal cliques with size of at least 3, including one clique of size 11, one clique of size 10, seven cliques of size 9, and so on down to 48 cliques of size 3. Many of these cliques overlap one another. For example, the clique of size 11 and the clique of size 10 share 9 proteins in common. We therefore created a network consisting of just these predicted cliques. This network, shown in http://noble.gs.washington.edu/proj/coco.Finally, we analyze the set of novel predictions for which neither protein is a member of a MIPS complex. At an FDR threshold of 10%, this set contains 686 CCPPs among 200 proteins. These predictions can be represented as an undirected graph, with proteins as nodes and predicted co-complex relationships as edges. We identified predicted new complexes as maximal cliques in this graph, where a In this paper, we developed multiple kernels from heterogeneous data sources and combined them in an SVM classifier to predict co-complexed protein pairs. We applied the diffusion kernel to the two-hybrid, APMS and genetic interaction networks, and we found that, in all three cases, a diffusion kernel performs much better than a linear kernel or the mutual clustering coefficient (MCC). A diffusion kernel computes the similarity between two vertices by summing over all paths connecting the two vertices with paths of shorter lengths receiving higher weight. In contrast, a linear kernel or MCC only considers paths of length 2. Our results indicate that taking into account the full network topology improves the prediction of CCPP edges. We also applied our prediction scheme to the protein pairs identified by two recent large scale APMS data sets 50 performance further from 0.859 to 0.937. We did not optimize the relative weights of the TPPK and RBF kernels. One future direction is to learn these weights by using semidefinite programming Different data sources provide complementary information, and each data source may have the best predictive power for a subset of data points. For instance, some protein pairs may have no sequence homologs, and some other protein pairs may not be included in the yeast two-hybrid screen experiments. Therefore, the combination of a variety of data sources has the potential to improve CCPP recognition. Kernel methods present a natural way to combine features by the summation of kernel matrices. Our results show that the TPPK applied to the summation of the sequence and diffusion kernels performs significantly better than either the sequence or the diffusion kernels alone. Inclusion of RBF kernels on five additional data sets improves the ROCThe method described here is specifically designed to work well in the presence of heterogeneous data—primary sequence, expression, interaction networks, etc. As such, the method can be applied fairly directly to other well-studied eukaryotic genomes. The minimal requirement for applying this method, or indeed any supervised learning algorithm, to a new organism is the availability of data  and labels (a set of known protein-protein interactions). In practice, the latter is much more difficult to come by. Typically, a genome with a sufficiently large set of high-quality interaction labels will likely also have available non-sequence data such as high-throughput interaction data and expression profiles.


The use of computational methods for predicting protein interaction networks will continue to grow with the number of fully sequenced genomes available. The Co-Conservation method, also known as the Phylogenetic profiles method, is a well-established computational tool for predicting functional relationships between proteins.E-value threshold used in defining homologs, predicted interactions are influenced by the similarity metric that is employed. We show that differences in predicted protein interactions are biologically meaningful, where judicious selection of reference genomes, or use of a new scoring scheme that explicitly considers reference genome relatedness, produces known protein interactions as well as predicted protein interactions involving coordinated biological processes that are not accessible using currently available databases.Here, we examined how various aspects of this method affect the accuracy and topology of protein interaction networks. We have shown that the choice of reference genome influences the number of predictions involving proteins of previously unknown function, the accuracy of predicted interactions, and the topology of predicted interaction networks. We show that while such results are relatively insensitive to the These studies should prove valuable for future studies seeking to further improve phylogenetic profiling methodologies as well for efforts to efficiently employ such methods to develop new biological insights. The best established method for function prediction is based on sequence homology to proteins of known function. Unfortunately, strictly homology-based predictions are of limited use due to the large number of homologous protein families with no known function for any single member -3. An alet al. , Phylogeet al. -11.et al. applied maximum likelihood statistical modeling for predicting functional protein linkages based on Phylogenetic profiling [et al. did a study using 16 different reference sets of genomes, using combinations of bacterial, archaea and eukaryotic genomes. They showed using a combination of bacterial and archaea genomes as a reference set could be enough to make accurate functional linkage predictions [et al. found phylogenetic relationships between genomes by using the first order of the genomes within profiles and then enumerating runs of consecutive matches to compute the accuracy of the probability of observing these phylogenetic relationships [et al. constructed Phylogenetic profiles based upon the presence or absence of neighboring protein pairs within a genome [et al. showed that accuracy of PPI predictions can be improved by using a set of genomes which are maximally distinct from one another [Currently several web-based databases compile predictions of protein-protein interactions (PPIs), e.g. PLEX , String rofiling . Their mrofiling . Jothi edictions . Cokus eionships . Zheng e another . We haveIt is sensible that inclusion of genomes from organisms that exist in similar environmental niches (i.e. rhizosphere bacteria), share certain phenotypic properties (i.e. motility), or that are from the same species (i.e. different strains of E. coli) might bias protein interaction network predictions in an undesirable manner. The challenge is that the extent of such biases remains uncharacterized, and thus methods for guiding the selection of relevant reference genomes are lacking. Here, we have examined such biases and then used our studies to developed a new scoring scheme to provide guidance for the selection of reference genomes in Phylogenetic profiling efforts.ij = 1 indicates a homolog exists between protein i in the target genome and a protein in a reference genome j, ii) calculating similarity measurements on the profile vectors for each pair of genes in the target genome, and iii) defining protein interactions in the target genome based on proteins sharing a profile similarity value greater than a threshold value. Using E. coli K12 as the target genome, we have evaluated how changing different aspects of this process, including the use of a new metric for defining similarity, affect predicted protein interaction networks.Phylogenetic profiling methods work by i) creating a Phylogenetic profile vector where PAll (268 bacteria), Selected (75 bacteria), Proteobacteria (130 bacteria), and Motile (104)), we identified the total number of PPI pairs that have at least one of the proteins labeled as unclassified. Our results showed the number of unclassified proteins was greatest when the reference genomes were either closely related phylogenetically (Proteobacteria) or phenotypically (Motile), regardless of the database used for assigning function. The Selected set of reference genomes contains only a single representative strain from bacterial species where more than one strain has been fully sequenced (i.e. E. coli). As such, this set of reference genomes can be considered the least closely related of all of the reference sets examined. Protein interaction networks created from the Selected set consistently contained the least number of proteins pairs involving with unknown function. It is of note that the EcoCyc [Many function prediction methods rely on the assumption that functionally related proteins interact ,18-21. Te EcoCyc  and ClusAll, Selected, Proteobacteria, Low GC Gram positive bacteria, High GC Gram positive bacteria, Motile, and Aerobic) for each of four different E-value thresholds across a range of similarity (confidence) requirements using EcoCyc and COG functional categories ) for seven sets of reference genomes . PPV was the best for Selected, All (inverse homology) and Aerobic, indicating that these reference genome sets interactions commonly occurred between proteins of the same function and was worst for Proteobacteria, Motile, and Low GC reference genome sets, with the All and High GC groups always remaining in between these two other groups. At restrictive E-value thresholds (E < 10-15), this result reinforced those reported above stating that reference genomes of maximum diversity created the most accurate (highest PPV) protein interaction networks. What was surprising was that these trends were observed even at less restrictive E-value thresholds, where it might be expected that proteins of lower similarity, and thus more likely to be false positives, would presumably be added to the network. In fact, the number of Co-Conserved proteins was similar regardless of the E-value threshold for all reference genomes  reference genome set and assessed predicted protein interactions (PPV) . Previous Phylogenetic profiling methods have either used protein vectors comprised of binary values   or vectoE-values ,24. We wc Figure . Note thE. coli K12 as a target genome indicated that the selection of reference organisms had a substantial effect on the overall properties of predicted protein interaction networks. An important additional issue was the details of how such effects were manifested at the level of specific protein-protein interactions and protein interaction clusters. To investigate this issue, we assessed the extent to which unique protein interaction clusters were predicted when different reference genomes were employed  or EAL (Glu-Ala-Leu) domains, and sensor proteins for the two component regulatory system  yet contribute to a common phenomenon (biofilms). This information is of substantial value to biological studies seeking to decipher complex phenotypes such as the biofilm phenotype examined here.Figure m Figure . This saormation ,26. Moreormation . When thInverse homology weighting to the performance of Prolinks and String databases both for predicting several well studied interaction networks and for overall PPV performance. Our scoring scheme weights the Phylogenetic profile vectors by taking into consideration the homology of the target genome versus the reference genome (Inverse homology) while the methods employed in the Prolinks and String databases do not consider the effect of the reference genome in their scoring scheme. It is of note that previously published data available from Prolinks showed that Prolinks had more linkages than other available sources at the time of implementation [All as the reference genome set since almost all of the publicly accessible databases use all available bacterial genomes at the time of their implementation. In previous studies, we showed that there were several well known PPI pairs involved in flagellum or biofilm processes that were not identified in publicly available databases [Inverse homology is used in the determination of similarity, all of the above proteins were contained within the predicted protein interaction network  among others. We showed that different sets of reference genomes produce substantially different results in the terms of the accuracy of predicted interactions (i.e. PPV), and that such results were relatively independent of the choice of E-value threshold employed to define homologs. This specific result demonstrates the need for flexibility in choosing among reference genomes when initiating Phylogenetic profiling based efforts for prediction protein interaction networks. One clear challenge here is that the selection of such reference genomes is not simple. Rather, this process requires knowledge of the relevant organisms, both in terms of their taxonomy and the specific phenotypes they express. To aid in this process, we introduced a new scoring scheme (Inverse homology) that considers the homology of the target and reference genomes, and thus places some emphasis on the evolutionary relationship of the relevant genomes. We showed that depending on the similarity metric used in combination with our Inverse homology scoring scheme, either the accuracy or the topology of the predicted protein interaction network was altered.Pellegrini organism . Since mInverse homology scoring scheme to the methods used by publicly available databases (i.e. Prolinks or String). The Inverse homology approach predicted known protein interactions in two separate biological processes  that were not predicted by existing methods.Our final studies were directed at understanding the extent to which these issues affected overall predictions and whether or not any observed differences provide new biological insights. We examined the topology of the network with various reference genomes and noted that when more closely related bacteria were selected, the clusters became larger with a higher degree of interconnectedness when compared to clusters derived from more distantly related reference genomes Table , 2, 3. IOverall, we have presented an evaluation of several key criteria affecting the accuracy and topology of protein interaction networks predicted by Phylogenetic profiling methods. We have shown that the choice of reference genome is of key importance and provided guidance, both in terms of different evaluations and the report of a new similarity scoring scheme, for future efforts seeking to further improve computational methods for predicting protein interactions as well as to use such methods for developing new biological understanding.Proteobacteria (130 bacteria), 2) Low G+C Gram positive bacteria (75 bacteria), 3)High G+C Gram positive bacteria (22 bacteria), 4) Selecting only one strain from those fully sequenced for each organism (Selected (75 bacteria)), 5) All the fully sequenced bacteria available on NCBI (All (268 bacteria)), 6) selecting based on oxygen requirement (Aerobic (91 bacteria), 7) Anaerobic (31 bacteria), and Facultative (107)), and 8) selecting based on Motility (Motile (104), and None-Motile (82)). In our evaluation we focused on a target genome E. coli K12 because a well curated dataset of protein functions is available [At the time of our implementation (June 2006), 268 complete microbial genomes were available through the National Center for Biotechnology Information (NCBI) and were downloaded from their ftp site . Phenotybacteria  bacteriaE-value of the top scoring sequence alignment between protein i and all the proteins of each reference genome j was assign to Eij. The Phylogentic profile was constructed as a vector with elements Pij, where Pij = 1 if a homolog exists  for same protein in genome j, otherwise Pij = 0. The number of the protein homologs varies depending upon our reference genome set and E-value threshold  would be limited to a few functional categories and poorly conserved proteins are likely uncharacterized, we eliminated such proteins prior to measuring profile similarities (described below). To check this assumption, we characterized the discarded proteins based on COG classifications as shown in Additional file Inverse homology. The Inverse homology was calculated by weighting the Phylogenetic profile vector by taking into consideration the homology of the target genome versus the reference genome. Given an E-value threshold, the homology Hi, j between two genomes was calculated as the ratio of number of homologs of each reference organism j to the number of proteins in the target genome i. For each protein i the target, if there was a homolog to reference protein j  then Pij = 1/ otherwise Pij = 0. Calculating the Pij in this way, rather than using binary values as originally thought [E-values [As an alternative to binary vectors, we also developed a weighting scheme which we refer to as  thought  or normaE-values , incorpoGiven a set of (weighted) Phylogenetic profiles, we can calculate the similarity between any pair of proteins using either Pearson correlation coefficient or Mutual Information. We describe each below.fX = (I/N), fY = (J/N), and fZ = (K/N)Similarity between two protein vectors using Pearson correlation coefficient was calculated as ,7,34fX =X, j overall reference genome j, J is the sum of PY, j over j. When the vectors are binary, K is the subset of genomes that contain homologs of both X and Y and N represent the total number of reference genomes. When the vectors are weighted by Inverse homology, K is the sum 1/Hi, j over the subset of genomes that contain homologs of both X and Y and N is the sum of 1/Hi, j over all j for target i.I is the sum of PSeveral studies have used Mutual Information (MI) to assess protein functional linkage ,7,34. ThfX, fY and fZ describe above, we used the equations below, as described by Wu et. al [Then to carry the sum using the quantities u et. al ,7,34Then2 when the vectors are binary and loge when the vectors are weighted by Inverse homology.Note: we use logE. coli K12 genome was analyzed, and the number of assigned pairs is shown in Table Networks were created and presented as graphs in which each protein is represented as a node and an interaction between proteins is represented by an edge. An edge exists between a pair of proteins whose Phylogenetic profiles similarity score exceed a given threshold. For separation of connected components of the network and building the clusters of proteins, breadth-first search (BFS) graph algorithms were used. The target E-value would affect the accuracy and performance of the Co-Conservation method. For determination and optimization of the E-value for each organism, four E-values were applied to determine when a homologous protein was present or absent, 1 × 10-15, 1 × 10-10, 1 × 10-5, and 1 × 10-3. An E-value was considered optimal if it had the maximum number of correctly linked proteins, as ranked by the selected scoring scheme. A correct linkage was defined by two proteins sharing the same biological process. For evaluation purposes we used on eight different types of reference genomes. Therefore, 32 combinations of different reference organism sets and various E-values thresholds were formed and were evaluated using COG functional categories and EcoCyc.We examined whether changing the BLASTP threshold i be the number of neighbors of node i and ki-1 be the number of nodes connected to neighbors of i. The clustering coefficient of node i is given asThe degree of a node in a graph is the number of edges connected to that node and proteins that are joined by an edge are said to be adjacent. A neighbor of a protein i is a protein adjacent to i. The clustering coefficient C indicates the degree to which k neighbors of a particular node are connected to each other. Let ki is the number of edges that exist between i, its neighbors and their neighbors [where neighbors . Then thIn order to measure the performance and reliability of our method over previous methods, we compared the number of interacting proteins, the number of predicted unknown proteins and the functional similarity of proteins sharing a protein-protein interaction.E. coli K12, based on Clusters of Orthologous Groups of proteins (COG) in NCBI [E. coli K12 [We evaluated the predicted protein-protein interaction data of  in NCBI  e.g. forcoli K12 ), biologcoli K12  five brocoli K12  functioncoli K12 , we extrInverse homology against the top 2,600 interactions from Prolinks by calculating the PPV using the EcoCyc database for E. coli K12  where the pair had the same functional category and a false positive (FP) where they belong to different functional categories. Similarly, for the EcoCyc database, proteins that appear in the same complex, pathway, operons, homologs and paralogous group are presumed to be true positives, while the other classified pairs were false positives. Sensitivity and Specificity are good predictive values but since the negative data set can not be defined, we used the Positive Predictive Value (PPV). The PPV was calculated as PPV = TP/(TP+FP). Finally, we compared our result to previous works such as String  and Prol2 Figure . We alsoAK, LH and RTG design the study. AK implemented the methods and analyzed the data. The manuscript was written by AK and edited by RTG and LH. RTG oversaw all biological aspects of the work and LH supervised the computational aspect.E-value thresholds. a) E-3 b) E-5 c) E-10 d) E-15. Positive Predicted Value (PPV) was calculated using EcoCyc functional category as described in Materials and Methods section.Comparison of different combinations of reference genomes set and Click here for fileAll). b) Proteins that appear in less than 10% of reference genomes (Reference genome is All). c) Proteins that appear in more than 90% of reference genomes (Reference genome is Motile). d) Proteins that appear on less than 10% of genomes reference genomes (Reference genome is Motile). E-value threshold is 10-5. COG functional categories are: Translation, ribosomal structure and biogenesis (J) ; Transcription (K); DNA replication, recombination and repair (L); Cell division and chromosome partitioning (D) ; Posttranslational modification, protein turnover, chaperones (O); Cell envelope biogenesis, outer membrane (M); Cell motility and secretion (N); Inorganic ion transport and metabolism (P); Signal transduction mechanism (T), Energy production and conversion (C); Carbohydrate transport and metabolism (G), Amino acid transport and metabolism (E); Nucleotide transport and metabolism (F); Coenzyme metabolism (I); Lipid metabolism (H); Secondary metabolites biosynthesis, transport and catabolism (Q); General function prediction only (R); Function unknown (S); Not classified (-); Intracellular trafficking, secretion, and vesicular transport (U), Defense mechanisms (V); RNA processing and modification (A).Functional classification of proteins appearing in more than 90% and less than 10% of organisms using COG. a) Proteins that appear in more than 90% of reference genomes (Reference genome is Click here for file


In the course of infection, viruses such as HIV-1 must enter a cell, travel to sites where they can hijack host machinery to transcribe their genes and translate their proteins, assemble, and then leave the cell again, all while evading the host immune system. Thus, successful infection depends on the pathogen's ability to manipulate the biological pathways and processes of the organism it infects. Interactions between HIV-encoded and human proteins provide one means by which HIV-1 can connect into cellular pathways to carry out these survival processes.We developed and applied a computational approach to predict interactions between HIV and human proteins based on structural similarity of 9 HIV-1 proteins to human proteins having known interactions. Using functional data from RNAi studies as a filter, we generated over 2000 interaction predictions between HIV proteins and 406 unique human proteins. Additional filtering based on Gene Ontology cellular component annotation reduced the number of predictions to 502 interactions involving 137 human proteins. We find numerous known interactions as well as novel interactions showing significant functional relevance based on supporting Gene Ontology and literature evidence.Understanding the interplay between HIV-1 and its human host will help in understanding the viral lifecycle and the ways in which this virus is able to manipulate its host. The results shown here provide a potential set of interactions that are amenable to further experimental manipulation as well as potential targets for therapeutic intervention. Pathogen invasion and survival requires that the pathogen interact with and manipulate its host. Human immunodefficiency virus type 1 (HIV-1) encodes only 15 proteins and must therefore rely on the host cell's machinery to accomplish vital tasks such as the transport of viral components through the cell and the transcription of viral genes . HIV-1 iS. cerevisiae and H. sapiens has been pursued for some time, it is only recently that host-pathogen interactions have come under greater scrutiny. Indeed, computational approaches are of significant value in the host-pathogen context as large-scale experimental characterization of these interactions is non-trivial [To understand the interface through which the pathogen connects with and manipulates its host requires knowledge of the molecular points of interaction between them. Specifically, knowledge of the protein interactions between pathogen and host is of particular value. While the prediction of protein interactions within species such as -trivial -6.P. falciparum and human using statistics about domains involved in within-species interactions [As a result of the need for computational approaches, several recent methods have been developed and applied to host-pathogen interactions, suggesting additional potential interactions in different host-pathogen systems. For instance, Dyer et al. predicted interactions between ractions . Also foractions . In the ractions . In the ractions .While of value, most approaches have not utilized the significant amount of protein structure information that is increasingly available. Specifically, rapid progress in structure determination technologies has led to the establishment and deposition of massive numbers of protein structures into the Protein Data Bank, with over 60,000 protein structures currently deposited . In combHere, we develop a map of interactions between HIV-1 and human proteins based on protein structural similarity. In this approach, we first retrieve structural similarity between host and pathogen proteins identified by an established method which compares known crystal structures. Human proteins identified as having a region of high structural similarity to an HIV protein are referred to as "HIV-similar." Next, we identify known interactions for these HIV-similar proteins, with the one or more human proteins that they interact with referred to as "targets." We then assume that HIV proteins have the same interactions as their human, HIV-similar counterparts, allowing HIV to plug into the host cell protein network at these points  Figure  and bothTo further narrow the list of likely interactions, we refined these results by requiring both the HIV-1 protein and the target human protein to be present in the same location within the cell, based on GO cellular component (CC) annotation. The refined set of predictions is shown in Figure Taking localization into account, gp41 has many more predicted interactors than any other HIV-1 protein. This is most likely due to the relatively large number of GO cellular component terms that were annotated to gp41 and also relevant to the host cell. Since gp41 is known to be found in more parts of the cell than other HIV-1 proteins, a larger number of human proteins were able to meet the co-localization criterion.The interaction predictions made by this method are specific for structures, and we note that different structures for a single protein may lead to different predictions about its interactions. Therefore, some information is lost if predictions are described at a gene level. Nevertheless, it may be of interest to consider interactions on a gene basis . The ESCRT complexes normally induce the formation of multivesicular bodies in the endosome, but can be recruited to the plasma membrane by Gag to aid in viral budding. Many members of the ESCRT machinery appear in our results, including VPS4A, STAM2, EEA1, RAB5A, and TSG101 . Our resTo further assess our predictions, we determined how many known interactions, curated within either HHPID or PIG, could have possibly been predicted using our method and the available data. First, in order for our approach to suggest a possible HIV-human interaction, the HIV protein must be represented among the crystal structures from PDB that are included in the Dali Database. In addition, any host factors predicted to interact with HIV-1 must have at least 1 known interaction with another human protein, and to be considered further, each of these must also have representative structures within Dali. Finally, in this work we included only those proteins that have been implicated in playing a role in HIV-1 infection through RNAi studies or studies of the protein composition of the virion. Since we removed any human target proteins that did not pass the Literature Filter, we did not make predictions for human proteins not mentioned in previous studies.A total of 319 known host-pathogen interactions satisfied these criteria. Sixty-two of these interactions (~19%) were predicted by our methodology, and are the set of predictions considered to be true positives . Cellular Component filtering of these random predictions gave a slight improvement with an average of 0.29% true positives Table . Using oWe also compared our predictions to those made by two previous computational studies predicting protein-protien interactions between HIV-1 and humans, namely the studies by Evans et al. and Tastan et al. ,10. SincThere were a few predictions that were shared between all methods. For our results before CC filtering, we found that there were 9 interactions predicted by all three methods Figure . Of thesWe have generated a map of potential protein-protein interactions between HIV-1 and its human host. The computational methodology used to create this map is based on the assumption that proteins with similar structures will share similar interaction partners. Thus HIV-1 proteins having a structure similar to one or more human proteins may potentially "plug in" to the host protein interactome at these points; providing the interface through which manipulation of downstream host processes can occur. From previous literature, many human proteins are known to play some role in HIV-1 infection. However, in most cases the nature of this role is unknown. Here, we provide specific predictions of how these human proteins may influence viral infection, namely by interacting with certain HIV-1 proteins.In principle, our approach is applicable to any host-pathogen system with known protein structures. HIV-1 has a small proteome, with most of its protein structures at least partially determined. In addition, HIV-1 also has a large set of identified interactions that can be used for model validation. While few pathogens currently have such rich data sets, continued progress in protein structure determination will help to remedy such deficiencies.Identification of points of modulation between a host and pathogen requires multiple lines of evidence. Computational methods can help integrate these data, providing a promising avenue for the discovery of novel host-pathogen interactions mediated by structural similarities as well as enhancing our understanding of functional relationships characterized through modern screening methods such as siRNA. Knowledge of the protein interaction network between the pathogen and human will not only further our basic understanding of pathogen survival mechanisms, but may also provide clinical targets to combat infectious disease.We used the Dali Database for structure comparisons (downloaded in January 2009), and the Human Protein Reference Database (HPRD), HHPID and PIG for protein interactions  -18,25,38We used the Dali database to ascertain structural similarity. Dali compares the 3D structural coordinates of two PDB entries by alignment of alpha carbon distance matrices, allowing for differences in domain order, and produces a structural similarity score ,16,17. TWe found known interactions between HIV-similar proteins and target human proteins, using data from the HPRD database, which contains literature curated interactions between pairs of human proteins . For eacTo reduce the number of predictions as well as add information from functional studies, predictions were filtered based on previous implication of the human protein's involvement in the HIV-1 infection process. One criterion was presence of the host protein in the HIV-1 virion. Host proteins known to be incorporated into or onto HIV-1 during budding were taken from several literature sources ,24,47. Thttp://www.affymetrix.com/products_services/arrays/specific/hgu133plus.affx#1_4[Another filtering criterion was the host protein's essentiality for HIV-1 infection. Recently, several large-scale experiments using siRNA or shRNA knockdowns to identify host proteins involved in the HIV-1 life cycle have been published -22. The To create a smaller and potentially more reliable list for further experimental validation, we further filtered the predictions based on shared sub-cellular localization. The Cellular Component (CC) Filtered dataset contains interaction predictions where the two proteins share Gene Ontology (GO) cellular component annotation. Pairs of HIV-1 and human proteins predicted to interact were only included in this dataset if both proteins were annotated by DAVID as being present in the same cellular compartment ,42. PairSince within Dali there may be multiple PDB structures representing the same protein, there is some redundancy in the interaction predictions. In certain cases, multiple PDB structures for the same HIV-1 protein were found to be similar to multiple PDB structures for an HIV-similar protein, leading to the same interaction predictions. Therefore, the predictions were counted as unique pairs of Uniprot accessions. In addition, for ease of viewing the predicted interactome, each node representing an HIV-1 protein is labeled with the protein name while each human protein is represented by Entrez Gene ID. To determine the correct mapping of PBD codes to HIV-1 proteins, the molecule name associated with each PDB chain was searched for keywords indicating the protein, with ambiguous cases treated on an individual basis. For example, PDB molecule names containing the word "capsid" but not "nucleocapsid" were assigned to the node "capsid." Furthermore, molecule names indicating polyproteins, such as those containing the phrase "gag-pol" were checked individually to determine which specific part of the polyprotein was represented by the entry. Two PDB structures were found to represent more than one mature HIV-1 protein: 1l6nA contains both capsid and matrix, while 1bajA contains capsid and p2 ,49; thesTo determine which predictions are true positives, PIG and HHPID entries for the predicted human interactors were examined to see if they contained the HIV-1 protein they were predicted to interact with ,38. Theslog10 for easier visualization.The Gene Ontology (GO) provides a system of terms to consistently describe and annotate gene products . GO termTwo forms of computational validation of the method were conducted. As it is not possible to predict all known interactions due to lack of protein structures, as well as other factors, we first determined the largest set of known interactions that it is theoretically possible to predict using our approach. To do this, we first determined the sets of all proteins that could be considered. This includes the set of all HIV-1 proteins in Dali (HIV set), the set of all human proteins that are represented in both Dali and HPRD (possible HIV-similar set), and the set of all human proteins in HPRD that are known to interact with at least one protein in the possible HIV-similar set as well as pass the literature filter (possible target set). Next, every pairwise combination of proteins in the HIV set and the possible target set was checked to see if it represented a known interaction curated in HHPID or PIG. The resulting number of true interactions that could have been found by the method was compared to the number of true positives that were actually found, both before and after filtering by cellular components.In the second approach, actual prediction results were compared to predictions based on randomly selected HIV-human protein pairs. The HIV-1 proteins were chosen from the 69 Uniprot accessions represented at least once by structures in the Dali Database. For human proteins, two different sets of human Uniprot proteins were created, one containing all the proteins in HPRD, and the other containing the subset of human proteins that also passed the Literature Filter. The set of all human proteins in HPRD consisted of 8582 proteins and was used to see the accuracy of purely random predictions, while the second set of 830 proteins was used to observe the effect of the Literature Filter.Since the structural similarity step was omitted, the predictions based on a human protein being similar to an HIV-1 protein and incorporated into the virion could not be simulated with the random selection procedure. We found that if we excluded this class of predictions from our real results, the number of unique predictions made was reduced to 2139, but all 62 true positives were still included. Therefore, we randomly selected 2139 pairs of HIV-1 proteins and human proteins from the entire HPRD, and a second set of 2139 pairs of HIV-1 proteins and Literature Filtered human proteins for evaluation. Next, any known interactions between the randomly chosen pairs were found using HHPID and PIG. Additionally, both the unfiltered and Literature Filtered random predictions were then subjected to the CC Filter to gauge the improvement due to this step of the method. The CC Filter reduced the number of predictions to a variable degree, depending on how many of the random predictions were annotated with the same GO cellular component term. The entire procedure was repeated 1000 times. The mean and standard error of the mean for each of the four variously filtered random prediction sets was calculated using R. The distributions of random predictions after Literature Filtering were approximately normal, so one-sided single sample t-tests were performed to determine if the method performed significantly better than random. In addition, we performed Wilcoxon signed-rank tests that do not make assumptions about normality. When comparing our results to random predictions that had undergone the same filtering steps, either the Literature Filter or both the Literature and CC Filters, the p-values were less than 2.2e-16 for all statistical tests. In addition, even when performing the randomization procedure 10000 times, none of the randomly selected interaction sets had a true positive rate higher than that observed in our results, suggesting a p-value of no greater than 0.0001.To compare our predictions to those made by Evans et al. and Tastan et al., we found the intersection of the prediction sets, counted by HIV-1 protein name and human Entrez Gene ID ,10. SincThe authors declare that they have no competing interests.JMD and SMG conceived and designed the study, JMD performed the research. JMD and SMG analyzed the results and wrote the manuscript. Both authors read and approved the final manuscript.Dali Similarities. Structural Similarities between HIV-1 and human proteins from the Dali Database. Column descriptions: HIV PDB code- PDB code of an HIV-1 protein in Dali, HIV Molecule Name- description of the HIV protein taken from the PDB molecule name, HIV GeneID- the Entrez GeneID of the HIV protein if available, HIV Uniprot- Uniprot accession of the HIV protein, human PDB code- PDB code of a structurally similar human protein, human Molecule Name- description of the human protein taken from the PDB molecule name, human GeneSymbol- the Entrez Gene Symbol of the human protein, human GeneID- the Entrez GeneID of the human protein if available, human Uniprot- Uniprot accession of the human protein, Structural Similarity Score- the z-score for the similarity between the HIV and human protein. This file is provided in tab-separated text format and may be viewed in a text editor or Excel.Click here for fileInteraction Predictions. Interaction predictions, before the CC Filter. Column descriptions: HIV PDB code- PDB code of an HIV-1 protein in Dali, HIV node name- protein name used to represent the HIV protein in the interaction network, HIV-sim Human PDB code- PDB code of a human protein similar to the HIV protein, HIV-sim Human Gene Symbol- the Entrez Gene Symbol of the human protein that is similar to the HIV protein, HIV-sim Human GeneID- the Entrez GeneID of the human protein that is similar to the HIV protein, HIV-sim Human Uniprot- the Uniprot accession of the human protein that is similar to the HIV protein, Human node Gene Symbol- the Entrez Gene Symbol of a human protein predicted to interact with the HIV protein. The Gene Symbol was used to represent the human protein in the interaction network, Human interactor GeneID- the Entrez GeneID of a human protein predicted to interact with the HIV protein, Human interactor Uniprot - the Uniprot accession of a human protein predicted to interact with the HIV protein, Source Datasets- paper(s) with support for a role of the human protein in HIV infection, True Positive?- whether or not the predicted interaction is already represented in PIG or NIAID, Type-whether the prediction was made because the human protein is known to interact with an HIV-similar protein or because the human protein is HIV-similar itself and known to be incorporated into the HIV virion, NIAID- HIV proteins listed in NIAID as interacting with the human protein, PIG- HIV proteins listed in PIG as interacting with the human protein. This file is provided in tab-separated text format and may be viewed in a text editor or Excel.Click here for fileFull Prediction Network. HIV-1 proteins that resemble human proteins are predicted to interact with the known interactors of the mimicked protein. The human proteins included in the prediction set have a supported role in HIV-1 infection or replication, either because they are incorporated into the HIV-1 virion or their reduced expression is known to prevent HIV-1 infection (node line color corresponds to source). Red lines represent predicted interactions that are already known to occur. This is an image file in .png format.Click here for fileInteraction Predictions after CC Filter. Interaction predictions after the CC Filter. Column headers are as in Additional file Click here for fileHIV-1 Protein Identifiers. The PDB codes and Uniprot accessions that correspond to each HIV-1 protein are given. This file is provided in tab-separated text format and may be viewed in a text editor or Excel.Click here for file


A new paradigm of biological investigation takes advantage of technologies that produce large high throughput datasets, including genome sequences, interactions of proteins, and gene expression. The ability of biologists to analyze and interpret such data relies on functional annotation of the included proteins, but even in highly characterized organisms many proteins can lack the functional evidence necessary to infer their biological relevance.Escherichia coli, Saccharomyces cerevisiae, and Plasmodium falciparum . The number of annotated genes is increased by PFP to over 90% for all of the genomes. Using the large coverage of the function annotation, we introduced the functional similarity networks which represent the functional space of the proteomes. Four different functional similarity networks are constructed for each proteome, one each by considering similarity in a single Gene Ontology (GO) category, i.e. Biological Process, Cellular Component, and Molecular Function, and another one by considering overall similarity with the funSim score. The functional similarity networks are shown to have higher modularity than the protein-protein interaction network. Moreover, the funSim score network is distinct from the single GO-score networks by showing a higher clustering degree exponent value and thus has a higher tendency to be hierarchical. In addition, examining function assignments to the protein-protein interaction network and local regions of genomes has identified numerous cases where subnetworks or local regions have functionally coherent proteins. These results will help interpreting interactions of proteins and gene orders in a genome. Several examples of both analyses are highlighted.Here we have applied high confidence function predictions from our automated prediction system, PFP, to three genome sequences, The analyses demonstrate that applying high confidence predictions from PFP can have a significant impact on a researchers' ability to interpret the immense biological data that are being generated today. The newly introduced functional similarity networks of the three organisms show different network properties as compared with the protein-protein interaction networks. The recent paradigm shift in molecular and systems biology to characterization of large sets of genes and proteins has been enabled by continual technological innovations, including fast sequencing technologies -3, arrayWe previously introduced PFP as a method for predicting Gene Ontology (GO) functional terms  for indiEscherichia coli, Saccharomyces cerevisiae (baker's yeast), and Plasmodium falciparum . The malaria genome is used as an example of a poorly annotated organism for which new annotation provides extensive interesting and useful functional knowledge. Taking advantage of PFP's larger function annotation coverage, more than 90% of proteins encoded in each genome are annotated. In order to investigate the structure of the functional space occupied by each proteome, we represented the mutual functional similarity of proteins in a form of network named the functional similarity network. To the best of our knowledge, this is the first of its kind introduced to investigate the structure of protein function space. Weston et al. proposed to consider the pairwise sequence similarity of many proteins in a database, which is named the protein similarity network, to improve the database search accuracy . To construct the function similarity networks for each organism, we performed an all-by-all pairwise comparison to find the funSim and category GOscore values for each unique protein pair.max and a slide value equal to 1/5 the window size. The window sizes were determined such that the number of genes for both strands in any window averaged between eight and ten. Genes included in the window were taken from the plus and minus strands individually and also from both strands together. Windows for which the category GO score was above 0.7 or the funSim was above 0.49 were analyzed for overrepresentation of GO functional terms by the method described below.To identify functionally similar regions of a genome, we used a sliding window approach. For each organism we used a unique window size  correction for multiple hypothesis testing:where q is the number of unique GO terms annotated to proteins in the cluster.The annotation gain for a subset of proteins is calculated as the percentage increase in the number of unique new statistically overrepresented annotations as compared to the number of previously known annotations.http://kiharalab.org/pfp and as a downloadable distribution as used in these analyses http://kiharalab.org/pfp/dist. In addition, the supplemental data including the function annotation by PFP to the three genomes and the PPI networks and networks statistics of the functional similarity networks and the PPI networks are available at our lab website http://kiharalab.org/func_network_suppl/.PFP is available as a web tool TH implemented the algorithms, conducted the experiments, and drafted the paper. MC analyzed the function prediction by PFP and the properties of the functional similarity networks. DK conceived of the study, participated in its design, coordination, and finalized the manuscript. All authors read and approved the final manuscript.


P<10−50). Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotation in this post-genomic era.The use of high-throughput techniques to generate large volumes of protein-protein interaction (PPI) data has increased the need for methods that systematically and automatically suggest functional relationships among proteins. In a yeast PPI network, previous work has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional association. In this study we improved the prediction scheme by developing a new algorithm and applied it on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting function-associated protein pairs. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as benchmarks to compare and evaluate the function relevance. The application of our algorithms to human PPI data yielded 4,233 significant functional associations among 1,754 proteins. Further functional comparisons between them allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made functional inferences from detailed analysis on one subcluster highly enriched in the TGF-β signaling pathway ( Due to advance in DNA sequencing, genes are being discovered at unprecedented speed, creating a need for annotating their functions. High-throughput mapping of protein-protein interaction (PPI) data is an example of functional genomics that enables rapid assignment of functional annotations by links between proteins which imply functional associations. However, due to noises inherent in the process of data generation PPIs can be visualized as a graph with proteins composing the nodes, and interactions composing the edges . Ample evidence exists that such a graph is nonrandom in the topologies of its connectivity N, the degree  for each protein node is fixed, but the interacting partners are randomly selected. This specifies the random network which we compare the real PPI data with. We randomly pick proteins X and Y (X with Y with X and Y share Suppose that in a PPI network of size X and Y have m common partners is a product of three factors: (i) m proteins can be chosen from any of the N proteins, and there are ii) the remaining X can occupy iii) Y can be in any X to have Y to have P1 is accurate.The total number of graphs in which proteins P1 becomes inaccurate. To determine the reason behind this and to devise a compensation, we note that in any set A of m common partners, proteins with more interactions will appear at a higher frequency. An extreme case is that if one protein interacts with most proteins in the network , it is hardly a surprise to find any two proteins sharing it as a common partner. Because it is easier to observe hub proteins as common partners and because P1 only takes into account the degree of nodes on average, the significance of P1 should be down-weighted when hub proteins are involved as common partners. Therefore, we came up with another algorithm (Algorithm II) to reduce the influence of hub proteins: under the condition that all proteins are randomly connected, we used the degree X and Y) to compute the probability that only X and Y, and we derived the probability as follows:However, a more realistic random control is to also keep the degree distribution the same as the real PPI network  was assigned with both P1 and P2) according to Algorithms I and II for 311,023 protein pairs that had at least one common neighbor, and plotted the distribution of the probabilities  randomly connect nodes (proteins) but keep the total number of edges (interactions) the same ; and (ii) in addition to (i), keep the number of interacting partners of each protein the same as in our real PPI network . Compared to simple randomization, for both Algorithms I and II, the truncated power law–preserving randomization produced a probability distribution more similar to that of the real PPI network  corresponds to a closer biological relationship. With the Gene Ontology (GO) annotations P1, and preliminarily determined functionally associated protein pairs . We noted that in the top 5,000 protein pairs, each 1,000 pairs always had a higher overlap rate than those beyond the top 5,000 pairs, and that the region of high overlap will give us a high level of confidence in presenting reliable predictions (P1 (−17.11) as the cutoff from Algorithm I. It was interesting that the probability perfectly matched the Bonferroni correction N = 7,362 is the size of the whole protein network. The false discovery rate (FDR) Ideally, given that dictions . Thus, wP2 is designed to reduce the influence of these hub proteins within the top 5,000 protein pairs selected by P1 as we believe that P2 can identify protein pairs whose lower P1 is caused by common neighbors that are hub proteins and remove them from the list of significant protein pairs. With GO and KEGG as the benchmarks, the utility of P2 is then confirmed by the following assertions: (i) the protein pairs with a good P2 (Group I) always have a lower FDR  than those without a good P2  the protein pairs with a good P2 (Group I) always have a lower FDR than the same number of top protein pairs ranked by P1 only  and rankings of functional association by P1 and P2 are significantly different (P<10−16), an additional cutoff from Algorithm II makes difference from merely tightening the cutoff from Algorithm I. As the cutoff for P2 changes, the difference in FDR between Groups I and II varies; the difference maximizes when the cutoff goes to −30.03, which is the value we used for the second cutoff from Algorithm II  and −11.33 for ln(P2)] and 0.92 from the totally randomized network [cutoffs: −6.42 (P1) and −13.10 (P2)].In a real PPI network, it is common to have many hub proteins with large numbers of interacting neighbors. rithm II a. ThereP1 only b, 0.83 P1) and −30.03 for ln(P2)], the power law–preserving random networks have on average 86 protein pairs as significant associations  have close functional relationships and we listed the top 10 pairs in P<10−16, two-sample proportion test). This comparison supports the notion that our method offers more reliable functional associations than the human PPI data itself does. Because only 21.6% of the 4,233 protein pairs interact directly in the PPI data, we believe that the rest of them provide additional functional information that is not revealed in the PPI data.The GO and KEGG-based FDR for 23,782 direct interactions is 0.57, which is significantly higher than our FDR of 0.35 , and 834 have KEGG pathway annotations. If a protein has at least one annotated significant partner , a list of annotation(s) from its partner(s) can be sorted by frequency and annotations occurring at the highest frequency are assigned to this protein  or a “10” for pairs with no significant P1. The purpose of using such a large value was to minimize background noise. Then the dissimilarity matrix was subjected to agglomerative hierarchical clustering with an unweighted pair-group average. The whole cluster is given in Because clustering can significantly improve the quality of functional inference In the cluster of 1,729 proteins, most of the functionally related proteins were correctly clustered into their corresponding functional modules, in which they are characterized by similar functions or the same pathway . The lar−54, according to the calculation from Ingenuity software (right-tailed Fisher's exact test). With respect to this extreme P value, we reasoned that probably all the cluster members cooperate to mediate signal transduction. To investigate the role of the other 10 proteins in the TGF-β signaling pathway, we generated a functional relationship network using Osprey software (http://biodata.mshri.on.ca/osprey) The TGF-β signaling pathway–related subcluster a has a The cluster and the association network a and 5bP values  that are worthy of further investigations , is the insensitivity to the high false positive rate of high-throughput PPI data. After adding 6086 randomly generated interactions , we were still able to recover on average 93.4% of significant protein pairs; furthermore, >90% of falsely generated “significant protein pairs” will become significant if we loosen the cutoffs of et al. (2000) which infers the function of a protein from it direct interacting neighbors in the PPI network We compared the performance of our prediction scheme with that of the direct prediction scheme used by Schwikowki Human proteins may have multiple functions and belong to different functional modules, so different signaling pathways may also have some pathway members in common. It is thus reasonable to assume that the overlap of distribution b–e, espm interacting partners , we do not think it provides more information worthy of further investigation.We also developed a new algorithm for computing the probabilities that three proteins share In conclusion, we proposed an improved method to predict protein functional association and make reliable functional annotations; we derived a cluster to investigate signaling pathways and suggest potential novel pathway members. We believe that with the explosion of available human PPI data, our method will contribute greatly to the functional research of human proteins.www.thebiogrid.org), we downloaded the human PPI data (version 2.20), which derived from both conventional focused studies (∼69.6%) and high-throughput studies  From the BioGRID . These databases are good references for evaluating functional association because of its reasonable coverage of the genome and its large number of categories, which makes it improbable to have random matching of pathways.With GO annotation , we defined the GO overlap rate as follows: We defined the KEGG overlap rate in the same way as above . We used the GO and KEGG overlap rates to assess the functional association of protein pairs: a higher overlap rate corresponds to a closer functional relationship.Suppose the GO and KEGG pathways are complete: if both proteins in each pair have KEGG pathway identifiers and qualified GO terms, we call them declared positive protein pairs. If they share at least one identifier (either GO or KEGG identifier), we consider this declared association true positive; otherwise we consider it false positive. Therefore, the FDR can be written as follows: This false discovery rate is used to assess the performance of our algorithm as we expect an improved annotation scheme will lower the proportion of wrong predictions among declared significant functional associations.P values for signaling pathways identified in our cluster.We used Ingenuity® Pathway Analysis (IPA) 5.0 software  to identify existing pathway members and calculate Text S1(0.12 MB DOC)Click here for additional data file.Figure S11 and P2 (two dimensions) from the human protein-protein interaction (PPI) network (a) and the randomized but truncated power-law preserving PPI network (b). The vertical and horizontal lines stand for the thresholds from Algorithms I and II, respectively. In a random PPI network (with truncated power-law), the expectation of significant protein associations is 86 (lower left in b) compared with 4,233 significant associations in the real PPI network (lower left in a).Density plot of the distributions of P(0.15 MB TIF)Click here for additional data file.Figure S2A cluster that consists of 1729 human proteins. Indices above protein names are their coordinates in this cluster.(0.08 MB PDF)Click here for additional data file.Figure S3n . (a) Estimated precise rate of predicted KEGG pathways given n. (b) The number of predictions for KEGG pathway we can make given n. (c) Estimated precise rate of predicted GO terms given n. (d) The number of predictions for GO terms we can make given n.Estimation of prediction precise rates and the number of predictions we can make given different (0.03 MB TIF)Click here for additional data file.Table S11.The 4233 significant protein pairs derived by our method. There are totally 1,729 human proteins and 25 nonhuman proteins. Protein pairs are ranked in terms of P(5.43 MB DOC)Click here for additional data file.Table S2Predictions of 466 KEGG pathways for 274 proteins and 123 GO annotations for 114 proteins. The 2nd column is the predicted KEGG and GO IDs for proteins in the 1st column, with 3rd column as corresponding KEGG pathway names and GO terms. Ratio is the number of significant partners with the assigned annotation(s) divided by the total number of significant partners.(0.40 MB DOC)Click here for additional data file.