Complex networks have recently become the focus of research in many fields. Their structure reveals crucial information for the nodes, how they connect and share information. In our work we analyze protein interaction networks as complex networks for their functional modular structure and later use that information in the functional annotation of proteins within the network. We propose several graph representations for the protein interaction network, each having different level of complexity and inclusion of the annotation information within the graph. We aim to explore what the benefits and the drawbacks of these proposed graphs are, when they are used in the function prediction process via clustering methods. For making this cluster based prediction, we adopt well established approaches for cluster detection in complex networks using most recent representative algorithms that have been proven as efficient in the task at hand. The experiments are performed using a purified and reliable Saccharomyces cerevisiae protein interaction network, which is then used to generate the different graph representations. Each of the graph representations is later analysed in combination with each of the clustering algorithms, which have been possibly modified and implemented to fit the specific graph. We evaluate results in regards of biological validity and function prediction performance. Our results indicate that the novel ways of presenting the complex graph improve the prediction process, although the computational complexity should be taken into account when deciding on a particular approach. A protein within a cell is rarely a single constituent of the mechanism that performs its function. It has been observed that proteins involved in the same cellular processes often interact with each other PPI data has the nature of networks. This provides a global view of the context of each protein. There is more information in a protein interaction network (PIN) compared to sequence or structure alone. A protein in a PIN is annotated with one or more functional terms. Multiple and sometimes unrelated annotations can occur due to multiple active binding sites or possibly multiple stable tertiary conformations of a protein. The annotation terms are commonly based on an ontology. A major effort in this direction is the Gene Ontology (GO) project We can now characterize the computational function prediction as the process of understanding the relationship between the protein's interaction context and its functions. Grouping proteins of the PIN into sets (clusters) which show greater similarity among proteins in the same cluster than in different clusters has been shown as an effective approach to accomplish this goal PINs have also been analyzed by extracting protein complexes, i.e. finding densely connected subgraphs within the network. To infer such complexes many methods have been proposed. The Markov Cluster algorithm (MCL) In this paper we address the problem of function prediction in twofold manner. First, we propose novel graph representations of the PIN each having different level of complexity and different inclusion of the annotation information within the graph. Second, we select state-of-the-art algorithms for cluster detection that have not yet been used on PINs and we examine their efficiency in detecting clusters within the different graph representations of the PIN as previously defined. Since we are interested in function prediction the exploration of these methods goes one step further in establishing efficient clustering in terms of accurate cluster based function prediction and establishing the benefits and the drawbacks of combining the methods with the different graph representations of the PIN in the functional annotation process. We conclude the paper with a discussion of what would be the recommended approach of predicting a function in the PIN depending on the priorities of the outcome i.e. what is the best experimental setup if the prediction is done network wide versus a prediction for a single  protein(s), and if the prediction accuracy is of higher importance than its coverage, and vice versa.High-throughput techniques are prone to detecting many false positive interactions, leading to a lot of noise and non-existing interactions in the databases. Furthermore, some of the databases are supplemented with interactions computationally derived with a method for protein interaction prediction, adding additional noise to the databases. Therefore, none of the available databases are perfectly reliable and the choice of a suitable database should be made very carefully.We conduct our experiments on Saccharomyces cerevisiae PPI data which are compiled from a number of established datasets used in previous research on PPI. Namely, we first merge the PPI datasets of Uetz As previously stated, PPI data has the properties of a network and therefore can be represented as a graph. We introduce several different graph representations of the PIN, each of which represents the information within the data at a different level. Our first goal is to explore the level of detail that is sufficient for effective clustering of the PIN and function prediction, and to show that the novel augmented representations significantly improve performance. We point out here that all graphs resulting from a PIN are undirected since an interaction itself is undirected. The different representations with ascending level of complexity are defined as follows.simple graph with i and j. The simple graph is unweighted. With this graph we use only the topology of the PIN to determine clusters. For our data we have The most basic definition of a PIN graph representation is through E and thus define a weighted graph W is a matrix whose elements The simplest way to enrich the previous representation is to add weights to edges from Content-based weights: a content-based weight calculation is one that assigns weight i and j, not taking their environment (the graph structure) into account. If i and j, Structure-based weights: a structure-based weight calculation is one that takes the context of the nodes i and j into account, but not the content of the nodes themselves, when calculating weight i and j so that the result contains all the structural information about these nodes. The structural information of the graph i and all neighbours of j, while the second part is the sum of all content-based weights between node j and all neighbours of i. PINs are known to have proteins that interact with many other, which gives rise to hubs within the graph representing the PIN. Hybrid weights: it combines both content-based and structure-based weights; a natural way of combining them is taking the average of the two:W1 and W2 are possible. We are pointing out that multiple definitions of weighting may make sense, and that, depending on the task, one may be more suitable than the other. We will show how the different weighting schemes influence the result of clustering and function prediction.protein-term graph in which the terms associated to proteins in the PIN become part of its representation. More specifically T is the set of all terms present within the PIN and each term it is represented as a node in the graph. tE is the set of edges jt is associated with protein i in the PIN. This definition of the representation and the set of additional edges tE takes into account additional edges only between protein nodes (V) and new term nodes (T), and no edges exist between two term nodes, as shown on V and E have the same definition as in the previous representations. The graph is unweighted.We define In this way functional relationships between the proteins in the PIN are directly included in the graph representation and therefore in the process of clustering and function prediction. When we create the protein-term graph for our data we have a total of 3390 nodes  graphs are defined as it and jt be the sets of terms associated with nodes i and j, respectively, then for edge The The modern science of networks has brought significant advances to our understanding of complex systems, with the organization of the vertices in clusters  being one of the most relevant features of the graphs representing such systems. The problem of detecting clusters is very hard and not yet satisfactory solved, and is in the focus of a large interdisciplinary scientific community ijA has different meaning for different graph representations. When we work with unweighted graphs ik and m are defined with i and j if connections are made at random but respecting node degrees is ic defines the cluster to which node i is attached and One of the biggest breakthroughs in cluster detection was the Girvan and Newman modularity function The “Fast Community” (FC) The proposed algorithm from Blondel et al. (BGLL) N of links of the network and on the degree of interconnectedness of the clusters, even in cases where clusters are unambiguously defined, characterizing these methods with a so called resolution limit t represents the time parameter of the random walk, equivalent to the Hamiltonian introduced by Reichard and Bornhodt t is equal to 1. The algorithm implementation suggested in Recently it has been shown that modularity optimization may fail to identify clusters smaller than a scale which depends on the total number Partitioning of nodes in a graph has the disadvantage of being incompatible with the existence of overlapping clusters, i.e. situations where nodes belong to several clusters. This overlap is known to be present at the interface between clusters, but can also be pervasive in the whole graph We adopt the method proposed in α is related to node i and 0 otherwise. The matrix B can be seen as an adjacency matrix of a bipartite network. The line graph is constructed with projection of the bipartite graph by taking all nodes of one type for the nodes of the projected graph. A link is added between two nodes in the projected graph if two nodes have at least one node of the other type in common in the original bipartite graph, resulting in the adjacency matrix The conversion of the graph from node to line is done as follows: first the node graph is represented using the incidence matrix ik is the degree of node i.By calculating the adjacency matrix as in α is incident on vertex j and has weight i has strength When we work with weighted node graphs, A is the normalized adjacency matrix, while in the case of weighted graphs A is the weight matrix W, r is teleportation or restart probability, X(t) is the probability vector for the random walker visiting a node at time t, and S is the starting probability vector . At beginning X(0) = S.The ability of random walks to generate dynamics and represent information flow in the network makes them suitable for usage in the clustering problem. Probability flow of random walks on graph are used for creation of efficient and accurate clustering method by Rosvall and Bergstrom (Infomap) There are few different methods in the literature for assigning terms to a query protein after clusters are determined. Each of the methods is based on calculating a score for each term associated with a node that belongs to the same cluster as the query node, and assigning to the query protein those terms that have a score greater or lower than a predefined threshold depending on the score type being used. In our work we tested hypergeometric enrichment P-value, chi-square statistics and terms frequency within the cluster as scores for predicting terms.t is calculated with:N is the number of nodes in the graph representing the PIN, T is the number of nodes in the graph that have term t assigned to them, C is the cluster size and t assigned to them. The terms enriched within the cluster  is given in details in the appendix of Clustering validation was performed using a synthetic benchmark graph as given in N is the total number of nodes in the network, n1 and n2 are the sizes of the two complexes , and k is the number of nodes they have in common. This measure is effective and good when evaluating a single clustering algorithm but for two or more algorithms the evaluation is time consuming as it requires extraction of the corresponding real complexes for each computed cluster.We use many different clustering algorithms that produce different clusters by size and structure for which we evaluate biological relevancy, in other words we test to confirm that the cluster structure has not arisen by chance. If a cluster is biologically relevant, the genes belonging to the same cluster should have similar biological functions i, given with the equation above, n is the number of distinct terms present in the cluster. If the nodes in the same cluster have consistent terms, the value of the functional entropy will be low, being zero when nodes have only one term. We performed the biological validation of our clustering algorithms using entropy. We retained only clusters with more than 2 nodes, and for each combination of graph representation and clustering algorithm we calculated the average entropy over all clusters.A more efficient way of testing functional homogeneity is through functional entropy. The entropy is calculated as the sum of the appearance frequencies of all function terms in the cluster, and multiplies the logarithm of those frequencies The calculated entropy values are shown in G3) the unannotated query protein assumption means that all edges to term nodes should be deleted. Once the clustering algorithm has been applied, for each term present in the query cluster (i.e. the cluster of the query protein) we calculate its rank according to ω. For example, for ω = 0, the query protein is assigned with all the functions present in its cluster. We change the threshold in the  range and compute the numbers for the four possible different classes which can occur during the assignment process:The effective evaluation of protein functional annotation is challenging. The lack of agreed measures and benchmarks used for assessment of the methods performance makes this task difficult. In our work we used the leave-one-out method when only one protein at time plays the role of a query protein. In the leave-one-out method a random annotation protein is selected and is considered as unannotated. This assumption for no terms present at the query protein affects different representations in different ways. For the unweighted representations no additional changes have to be made, while weighted graphs should be altered since the weight computation is no longer possible as defined by the corresponding equations. Specifically if the representation uses the content based weight its value is substituted with the structure based weight and everything else remains the same. For the Protein-Term representation (True Positive (TP): When annotation is assigned and is part of the true annotation setTrue Negative (TN): When annotation is not assigned to the protein and is not part of the true annotation setFalse Positive (FP): When annotation is assigned but is not part of the true annotation setFalse Negative (FN): When annotation is not assigned but is part of the true annotation setEach annotation is assigned to one of the four classes. Using the number of annotations in each class (given in brackets above) we can calculate the following statistical measures:ω = 0 to ω = 0.9 with 0.1 step.We performed functional annotation for each combination of a clustering algorithm and a graph representations of our Saccharomyces cerevisiae PIN. We can see from the results shown on If we average the AUC values for a single algorithm over all graph representations  the top ω = 0. These values are a general indicator of the behaviour of the corresponding annotation process. The EdgeCluster algorithm shows much greater false positive rate as compared to the next in line (according to AvgAUC) Infomap. In fact, Infomap has the overall lowest levels of false positive rates with any graph representation. This means that Infomap performs very stringent clustering of the PIN which results in clusters that are poor in terms of function (term) diversity therefore missing out on part of the functions (terms) which should be associated with a query protein. This leads to a very precise, but incomplete view of the annotation set of the query protein. On the other hand EdgeCluster, timeBGLL, BGLL, and FC achieve much higher sensitivity at the price of a high false positive rate, which means that the annotation set view is much richer but more noisy as compared to Infomap. All of these results are due to the fact that the ratio between the number of clusters generated with Infomap and the other algorithms  is approximately 2.5∶1.The performance of the algorithms on the different graph representations proposed in this research is consistent in all the experiments as can be seen in In summary and considering the goals defined our results show that all of the proposed novel representations yield a significant improvement in the function prediction performance over the simple unweighted graph representation. The Protein-Term graph representation is the most informative one and if computational resources are not scarce it is the representation that should be used for the prediction. The next in line is the FFC graph representation, followed by the hybrid weighted graph representation. The ease of further augmentation of these two representation (for example with similarity metrics based on GO instead of using a simple Jaccard index) is their added value and they can be further improved to maximize the annotation prediction performance. All of the clustering algorithms used in this paper perform very good on the PIN, as it was shown in the clustering validation section, with Infomap being the best in that context. In terms of using these clustering algorithms in the function prediction the most accurate one is the Infomap algorithm, while edgeCluster and timeBGLL have the highest coverage.S. cerevisiae network has between 37800 and 75500 protein interactions S. cerevisiae, which means there are potentially unknown regions of the network which can explain the high false positive rates and low sensitivity stated before.As a final note we point out to another potential problem in the process of function prediction using clustering, namely the completeness. It has been estimated that the complete Complex protein interaction networks reveal graph properties that can be analysed in terms of functional modules associated with the biological function they perform. In our work we investigated the power of the novel algorithm for complex network clustering combined with novel graph representations of the protein interaction networks, and assess their possibilities for protein function prediction via clustering. We show that using these algorithms we can gain significant knowledge for the modular structure of the network. As these networks carry not only interaction information but also annotations the different representations we propose augment to the prediction process by including this information in the clustering of the network.The results from our experiments validate the augmented graph representation approach. Even the simplest augmentation i.e. the different weighted graph representations of the PIN significantly improve the results of the function prediction. Our experiments were performed using the simple normalized Jaccard Index as a weighting factor and we are confident that results can be even further improved using a more sophisticated weighting scheme. We used the same weighting when we further augmented the graph representation by adding artificial edges to take into account the well known fact that protein interaction networks to this date are still not completely captured by the experimental methods used for their construction. This representation is very complex and is computationally exhaustive but the potential of uncovering new knowledge is significantly increased. Our experiments showed that the most informative representation is the one where we generate a graph in which every single term associated with a protein becomes a node and the association of proteins and terms is represented by adding an edge between each pair. The power of unravelling the functions of a query protein of this representation is the greatest of all proposed representations, but also the same holds for the computational complexity.In general if one would like to perform a network wide annotation, usage of the weighted graph representations would be recommended, while the exploration of a single protein, or a small group of proteins, should the performed using either the full functional connected graphs or the protein-terms graph. In terms of selecting a clustering algorithm our results showed that Infomap has the best performance in determining the modular structure of a PIN and is also the most accurate of all tested algorithms. However, the high accuracy comes with the price of low coverage (i.e. the inability to discover a larger set of functions associated with a query protein). The opposite holds for the timeBGLL and EdgeCluster algorithms. Depending on the required results one can choose either a random walk and map algorithm (Infomap) if the priority is to get a narrow set of accurate protein functions, or either an edge clustering/overlapping clusters algorithm (EdgeCluster) or a multi-resolution algorithm (timeBGLL) if coverage of the possible functions is of bigger importance.File S1Matlab code for generation of the graph representations.(ZIP)Click here for additional data file.


The prediction of protein complexes from high-throughput protein-protein interaction (PPI) data remains an important challenge in bioinformatics. Three groups of complexes have been identified as problematic to discover. First, many complexes are sparsely connected in the PPI network, and do not form dense clusters that can be derived by clustering algorithms. Second, many complexes are embedded within highly-connected regions of the PPI network, which makes it difficult to accurately delimit their boundaries. Third, many complexes are small (composed of two or three distinct proteins), so that traditional topological markers such as density are ineffective.We have previously proposed three approaches to address these challenges. First, Supervised Weighting of Composite Networks (SWC) integrates diverse data sources with supervised weighting, and successfully fills in missing co-complex edges in sparse complexes to allow them to be predicted. Second, network decomposition (DECOMP) splits the PPI network into spatially- and temporally-coherent subnetworks, allowing complexes embedded within highly-connected regions to be more clearly demarcated. Finally, Size-Specific Supervised Weighting (SSS) integrates diverse data sources with supervised learning to weight edges in a size-specific manner—of being in a small complex versus a large complex—and improves the prediction of small complexes. Here we integrate these three approaches into a single system. We test the integrated approach on the prediction of yeast and human complexes, and show that it outperforms SWC, DECOMP, or SSS when run individually, achieving the highest precision and recall levels.Three groups of protein complexes remain challenging to predict from PPI data: sparse complexes, embedded complexes, and small complexes. Our previous approaches have addressed each of these challenges individually, through data integration, PPI-network decomposition, and supervised learning. Here we integrate these approaches into a single complex-discovery system, which improves the prediction of all three types of challenging complexes. With our approach, protein complexes can be more accurately and comprehensively predicted, allowing a clearer elucidation of the modular machinery of the cell.This article was reviewed by Prof. Masanori Arita and Dr. Yang Liu (nominated by Prof. Charles DeLisi). Protein complexes, which are stoichiometrically-stable structures consisting of multiple proteins that bind (interact) together, participate in many cellular processes. Determining the set of cellular complexes can help to elucidate the organization of cellular processes and their functional mechanisms.Many approaches have been proposed to derive complexes from high-throughput protein-protein interaction (PPI) data, typically by searching for dense clusters in the PPI network that correspond to groups of interacting proteins. For example, Clique Finder (CFinder ) and CluIn a previous paper  we descrWe have proposed three approaches that can help to address these problems. First, Supervised Weighting of Composite Networks (SWC ) addressSecond, decomposing PPI networks into spatially- and temporally-coherent subnetworks (DECOMP ) addressThird, Size-Specific Supervised Weighting (SSS ) addressAlthough SWC and DECOMP both improve the prediction of large complexes in general, they have been shown to give the largest improvements among the complexes that they are designed for: sparse complexes for SWC, and complexes embedded in dense regions for DECOMP. The third technique, SSS, targets another separate group of complexes, the small complexes. Thus, here we combine these three techniques into a single system that targets all three groups of challenging complexes, as this is likely to give a performance boost in complex discovery over using any single one of these techniques. Figure SWC+DECOMP+SSS: integrated approach consisting of SWC, DECOMP, and SSSSWC: Supervised Weighting of Composite network, using six clustering algorithms combined with majority votingDECOMP: decomposition of PPI network, using six clustering algorithms combined with majority votingSSS: Size-Specific Supervised WeightingPPI+COMBINE: PPI network weighted by reliability, using six clustering algorithms combined with majority votingPPI+clustering algorithm: PPI network weighted by reliability, using a single clustering algorithmWe compare the performance of the following approaches:t% of the complexes are selected for testing, while all the remaining complexes are used for training. Thus we use a large percentage of test complexes t%=90 %, giving 41 training complexes in yeast, and 183 training complexes in human. Each edge  in the network is given a class label co-complex if u and v are in the same training complex, otherwise its class label is non-co-complex. For SSS, the edges labeled co-complex are further split into two subclasses, small-co-complex and large-co-complex, for edges in small complexes (composed of two or three distinct proteins) and large complexes (composed of at least four distinct proteins), respectively. For the supervised approaches, learning is performed using these labels, and the edges of the entire network are then weighted using the learned models. The top-weighted k edges from the network are then used by the clustering algorithms to predict complexes. In our experiments we use k=20000 for SWC and DECOMP, and k=10000 for SSS (as described in their respective papers).We perform random sub-sampling cross-validation, repeated over ten rounds, using manually-curated complexes as reference complexes for training and testing. For yeast, we use the CYC2008  set whicP is ranked by its score. To obtain a precision-recall graph, we calculate and plot the precision and recall of the predicted clusters at various cluster-score thresholds. Given a set of predicted clusters P={P1,P2,…}, a set of test reference complexes C={C1,C2,…}, and a set of training reference complexes T={T1,T2,…}, the recall and precision at score threshold s are defined as follows:We use precision-recall graphs to evaluate how well the predicted clusters match the test complexes. Each cluster sm_match, and large complexes must be matched by large clusters with a different threshold of lg_match. We define lg_match=0.75 for large yeast complexes, lg_match=0.5 for large human complexes , and sm_match=1 for small complexes in both yeast and human.The precision of clusters is calculated only among those clusters that do not match a training complex, to eliminate the bias of the supervised approaches for predicting training complexes well. We require small complexes to be matched perfectly, as a mismatch of just one protein in a small complex may render the prediction less useful; on the other hand we allow a slight tolerance for mismatch for large complexes. Thus we require that small complexes must be matched by small clusters with a match threshold of Figure 22% to about 20 %. However, precision is improved, as the many false-positive small clusters are removed. For our integrated approach(SWC+DECOMP+SSS), the removal of small clusters means removing those clusters generated by SSS. We still achieve higher precision and recall than the other approaches, showing that our integrated approach still outperforms other approaches when considering large complexes only. Moreover, without removing small clusters, our integrated approach maintains high precision as it uses a specialized approach, SSS, to predict small complexes. We noticed that the generated small clusters may depress the precision, as many of them are false positives. Figures 2Figure 33% only. However, the improvement in precision is substantial: for example, PPI+COMBINE sees more than fivefold increase in precision at many points in the graph. This reveals an issue in complex prediction which is more obvious in human but still apparent in yeast: predicting small complexes alongside large ones means accepting a drop in precision due to large numbers of false-positive small clusters; while improving precision by excluding small clusters means that no small complexes can be predicted. On the other hand, our integrated approach uses a specialized approach, SSS, to generate the small clusters separately from the large ones, which allows effective prediction of the small complexes while still maintaining high precision levels.Figures 3% and 5 % of complexes in yeast and human respectively; the remaining complexes are challenging in some way, with the majority of them falling into the small-complex category.To investigate the performance of our integrated approach with respect to the three challenges that we highlighted, we stratify the reference complexes in terms of their sizes, extraneous edges, and densities. First, to quantify whether a complex is embedded within a highly-connected region of the PPI network, we derive EXT, the number of external proteins that are highly connected to it, defined as being connected to at least half of the proteins in the complex. Second, to quantify how sparse a complex is, we derive DENS, the density of each complex, defined as the number of PPI edges in the complex divided by the total number of possible edges in the complex. In our analysis, we stratify the complexes into large and small complexes, and further stratify the large complexes into low, medium, and high DENS , and low and high EXT (corresponding to EXT ≤3 and >3 respectively), to give seven total strata . Figures ,  spreads out the improvements among the complexes in the different strata, showing that the different approaches complement each other to predict different types of challenging complexes.Here we investigate the number and quality of novel complexes predicted by our approaches. For the supervised approaches, we use the entire sets of reference complexes for training. We keep only predicted complexes that are novel, unique, and high-confidence. First, predicted complexes that are similar to each other are filtered to keep only the highest-scoring one. Next, we keep only the top-scoring predictions such that the precision of these predictions (i.e. proportion of predictions that match a reference complex) is greater than 0.4. Finally, we keep only novel predictions by removing those that match a reference complex. We use a Jaccard similarity threshold of 0.5 in the above procedure for matching.We measure the quality of these novel predictions by their semantic coherence in each of the three GO classes, biological process (BP), cellular compartment (CC), and molecular function (MF). First, we use the most informative common ancestor method to calculate the semantic similarity between two GO terms . Then weFigure 8Figure 8An example novel human complex discovered by our integrated approach consists of proteins PELP1, SENP3, TEX10, and LASIL1. This complex is not part of our reference complexes, but is validated as four out of five members of a recently-characterized 5FMC complex . This coThree open problems remain within protein-complex prediction. First, many complexes are sparsely connected in the PPI network, and so do not form dense clusters that can be derived by clustering algorithms. Second, many complexes are embedded within highly-connected regions of the PPI network, which makes it difficult for clustering algorithms to accurately delimit their boundaries. Third, many complexes are small (composed of two or three distinct proteins), so that traditional topological markers such as density or sparse boundaries are ineffective.Previously, we proposed three approaches for addressing each of these challenges. First, Supervised Weighting of Composite Networks (SWC) integrates diverse data sources with supervised learning to weight edges with their posterior probabilities of being co-complex. SWC was shown to improve the prediction of sparse complexes. Next, PPI network decomposition using GO terms and hub removal (DECOMP) was shown to improve the prediction of complexes embedded within highly-connected regions. Finally, Size-Specific Supervised Weighting (SSS) integrates diverse data sources and topological features with supervised weighting to weight edges with their posterior probabilities of belonging to small complexes. SSS was shown to improve the prediction of small complexes.Here we integrate these three approaches into a single system. SWC, DECOMP, and SSS are run independently on the input PPI data and other data sources, and the resulting clusters are scaled to standardize their scores, then combined using majority voting. We test the integrated approach on the prediction of yeast and human complexes, and show that it outperforms SWC, DECOMP, or SSS when run individually, achieving the highest recall, and the highest precision at all recall levels.We also investigate which complexes benefit most from our individual approaches and the integrated approach, compared to a baseline of running a set of clustering algorithms on a reliability-weighted PPI network. In both yeast and human, we find that SWC improves the prediction of sparse complexes, DECOMP improves the prediction of embedded complexes, and SSS improves the prediction of small complexes. The integrated approach combines these improvements and distributes them among the different types of challenging complexes.Although we have taken great strides in tackling the three challenges we highlight within complex prediction, and have obtained substantial improvements in prediction accuracy and recall as a result, there remains room for further improvement. Moreover, as increasing amounts of PPI data become available for other organisms, the techniques that we propose will be useful in enabling the discovery of novel complexes in those organisms.http://compbio.ddns.comp.nus.edu.sg/~cherny/IntegratedComplexPrediction/.The integrated complex-prediction software package and data files are available at In this section we describe how we integrate our three techniques, SWC, DECOMP, and SSS, into a single system. We first describe the data sources and clustering algorithms used, then describe the integrated system.a,b) with score s, a is related to b with score s, according to that data source. For both yeast and human, the following data sources are used:PPI: We take the union of physical PPIs from three databases: BioGRID [e as the fraction of its detected PPIs where both interaction partners share at least one high-level cellular-component Gene Ontology term. Then the reliability of an interaction  is estimated as: BioGRID , IntAct  BioGRID  and MINT BioGRID  , and ne,a,b is the number of times that experimental method e detected interaction . The scores from the Consolidated dataset are discretized into ten equally-spaced bins , each of which is considered as a separate experimental method in our scoring scheme. We avoid duplicate counting of evidences across the datasets by using their publication IDs .where STRING: STRING predicts functional associations between proteins using various evidence types, including genetic context and experimental data [tal data  , using the Jaccard similarity of the sets of papers that a pair  appears in:Ax is the set of PubMed papers that contain protein x. For yeast, that would be the papers that contain the gene name or open reading frame (ORF) ID of x as well as the word “cerevisiae”; for human that would be the papers that contain the gene name or Uniprot ID of x as well as the words “human” or “sapiens”.where Our three approaches use different sets of data features . These features are derived from three different data sources—PPI, functional association, and literature co-occurrence—and their topological characteristics. Each data source provides a list of scored protein pairs: for each pair of proteins , or the sum of the scores of the outgoing edges from the pair:w is the data source score of edge , Na is the set of all neighbours of a, excluding a.where NBC: The neighborhood connectivity of the protein pair , defined as the weighted density of all neighbors of the protein pair excluding the pair themselves:w is the data source score of edge ; Na,b is the set of all neighbours of a and b, excluding a and b themselves; λ is a dampening factor.where SHARED: The extent of shared neighbors between the protein pair, derived using the Iterative AdjustCD function (with two iterations) [rations) .For each protein pair in each data source, we derive three topological features: degree (DEG), shared neighbors (SHARED), and neighborhood connectivity (NBC). For each data source, the edge weight used to calculate these topological features is the data source score of the edge.ISO), represents the probability that the protein pair  is in a size-2 or size-3 clique which is isolated from the rest of the network. It is derived from an initial calculation of the small-co-complex posterior probabilities  [hm (MCL)  simulateRestricted Neighborhood Search Clustering (RNSC) [g (RNSC)  starts fIPCA [IPCA  starts fClustering by Maximal Cliques (CMC) [es (CMC)  first geClustering with Overlapping Neighborhood Expansion (ClusterONE) [sterONE)  starts fCoach [Coach  adopts tC and D to be similar if Jaccard≥0.75, where Jaccard is the Jaccard similarity between the proteins contained in C and D:VX is the set of proteins contained in X.SWC and DECOMP both use a simple voting-based aggregative strategy, called COMBINE, to take the union of the clusters produced by multiple clustering algorithms. If two or more clusters are found to be similar to each other, then only the cluster with the highest weighted density is kept, and its score is its weighted density multiplied by the number of algorithms that produced the group of similar clusters; otherwise its score is its weighted density as usual. The cluster scores are then normalized to a maximum of 1 by dividing by the number of clustering algorithms used. We define two clusters Figure SHAREDPPI, STRING, and LIT); here we incorporate the PPI feature, as the reliability of a PPI is an effective indicator of a co-complex relationship.First, SWC  integratNhub interaction partners  are annotated to more than NGO proteins . For each of the clustering algorithms, the algorithm is run on the subnetworks, the clusters from the subnetworks are re-combined, and hubs are re-added to those clusters they are highly-connected to. Finally, the resulting clusters from the various clustering algorithms are combined with majority voting to produce a set of clusters. Each cluster is scored by its weighted density (weights being the PPI reliabilities), multiplied by the number of clustering algorithms that produced it, and normalized to 1. We keep only clusters of size four or larger. Note that the original PPI decomposition approach . Then iribed in . The oriNext, SSS  integratd, while those generated by SSS are scaled by a factor s . In our experiments we used d=0.6,s=1.0 for yeast, and d=0.6,s=0.3 for human. These factors were obtained by observing the relationship between scores and precision levels in the cross-validation results for each approach (e.g. a cluster predicted by DECOMP with a score of 0.6 obtained roughly the same precision as a cluster predicted by SWC with a score of 1.0). Then we take the union of the clusters produced by the three approaches. If a cluster from two or more approaches are found to be similar to each other (Jaccard similarity ≥0.75), we sum its scores from the different approaches.Finally, the clusters produced by the three approaches are combined, also using the voting-based aggregative strategy. However, since each approach scores its clusters in a different manner, we first scale their scores to make them comparable. The clusters generated by DECOMP are scaled by a factor Prof. Masanori Arita, National Institute of Genetics, JapanBMC Syst Biol. 2014, 8(Suppl 5):S3BMC Syst Biol. 2012;6 Suppl 2:S13Proteome Sci. 2011 Oct 14;9 Suppl 1:S15.The manuscript describes the summarizing work of the authors’ previous three published results. The current work is an integration or meta-tool approach by dissecting the given PPI dataset by complex sizes and other characters.Overall, the approach taken is sound and the results are well justified. The flow of the manuscript is, however, extremely similar to their 2nd work in 2012. There is no paragraph copied or identical but essentially, methods and assessments are the same. This in part inevitable because the approach taken is similar. Still, to emphasize novelty, I would like to encourage authors to add more insights obtained by this meta approach. In fact, their paper in 2012 introduces more detailed analysis on each complex identified. Along this line, suggested idea is focusing on some human or yeast specific protein complexes compares their function with the previous work in 2012. what I could not fully understand was the slightly different use of STRING database. STRING is in itself a meta-tool for PPI, and was used in the previous approach too (2012 paper). Is its usage different this time? Then what is the reason?Indeed, the main contribution of this paper is to integrate our previously-presented complex-prediction methods into a single tool, rather than to propose an entirely new complex-prediction method. Since this work integrates the 3 previous methods, it does not predict any novel complexes that were not predicted by any of the 3 methods. Instead, we show that the power and accuracy of the integrated approach is much improved, especially for human complexes. Nevertheless we have revised the paper to add an example validated complex (5FMC) which was low-scoring using the individual approaches, but now become higher-scoring after integrating the 3 approaches together (due to the voting-based scoring).The STRING database is used in the same way in SWC in the 2012 paper and in SWC in the current paper: as a datasource for functional associations, not necessarily PPIs (this is the consequence of how STRING scores its associations). So we consider the STRING data as functional associations to be quite orthogonal to our PPI data. The SSS method further includes additional topological features derived from STRING: the degree, neighbourhood connectivity, and shared neighbours, in the STRING network. This is because these features are useful in distinguishing true small complexes from false positives: while many size-2 and size-3 groups of proteins form isolated clusters in the PPI network by chance, it is less likely that they also form isolated clusters in the STRING network and the LIT network. We have revised the paper to describe the use of features in the different methods.Dr. Yang Liu (nominated by Prof. Charles DeLisi), Boston University, USAThis paper integrates three approaches that they identified before into a single system to predict problematic complexes. They apply the system to both yeast and human PPI networks and can achieve higher accuracy and more comprehensive prediction, compared with three individual methods.In Background, instead of only introducing their own work, the authors may also consider talking about other similar studies to predict complexes, especially for recent years.hub, d, s etc. Without any explanation, it makes readers confused about how to choose these parameters to get the best performance. Are they based on experimental tests, or some statistical calculation? The authors may need to demonstrate how they make the selection.It seems that methods proposed in this paper are not parameter-free, not only for three individual ones, but also for the integrated approach. There are too many parameters to fix, like N_The authors claim that integrated method outperforms three individual methods, which seems obvious because those three methods try to address three different challenges, and an integrated version could no doubt work better. Have the authors tried to compare their method to other independent complexes prediction approach?Major:We have revised the hub and N_go, were determined experimentally in their original papers; we have modified our paper to clarify this. The parameters specific to the integration approach are d and s: these are scaling parameters to calibrate the scores of DECOMP and SSS, and their recommended values have been found through cross-validation experiments.Parameters specific to the individual approaches, like N_Indeed, Figure 2323


Drugs that simultaneously target multiple proteins often improve efficacy, particularly in the treatment of complex diseases such as cancers and central nervous system disorders. Many approaches have been proposed to identify the potential targets of a drug. Recently, we have introduced Space-Related Pharmamotif (SRPmotif) method to recognize the proteins that share similar binding environments. In addition, compounds with similar topology may bind to similar proteins and have similar protein-compound interactions. However, few studies have focused on exploring the relationships between binding environments and protein-compound interactions, which is important for understanding molecular binding mechanisms and helpful to be used in discovering drug repurposing.50 of 56 and 25 flavonoid-kinase inhibitions are less than 10 μM and 1 μM, respectively. Furthermore, these experimental results suggest that these flavonoids can be used as anticancer compounds, such as oral and colorectal cancer drugs.In this study, we propose a new concept of "Homopharma", combining similar binding environments and protein-compound interaction profiles, to explore the molecular binding mechanisms and drug repurposing. A Homopharma consists of a set of proteins which have the conserved binding environment and a set of compounds that share similar structures and functional groups. These proteins and compounds present conserved interactions and similar physicochemical properties. Therefore, these compounds are often able to inhibit the proteins in a Homopharma. Our experimental results show that the proteins and compounds in a Homopharma often have similar protein-compound interactions, comprising conserved specific residues and functional sites. Based on the Homopharma concept, we selected four flavonoid derivatives and 32 human protein kinases for enzymatic profiling. Among these 128 bioassays, the ICThe experimental results show that the Homopharma is useful for identifying key binding environments of proteins and compounds and discovering new inhibitory effects. We believe that the Homopharma concept can have the potential for understanding molecular binding mechanisms and providing new clues for drug development. Developing a new drug is difficult and takes on average of 13 years as well as US$1.8 billion -4. Some One challenge of targeted polypharmacology is to identify the molecular targets that bind the compound. To recognize the proteins with similar binding sites of a given protein sequence or structure, the computational approaches of searching the protein sequence or structure databases are usually utilized -21. As tin vitro enzymatic profiling. The experimental results identified that the IC50 values of 56 and 25 flavonoid-kinase inhibitions are less than 10 μM and 1 μM, respectively. Some novel protein-compound interactions also suggest that these flavonoids could be used as anticancer compounds such as oral and colorectal cancer drugs. These results show that the concept Homopharma is not only useful for identifying potential targets of compounds, but can also reveal the key binding environment. Furthermore, it would be helpful for discovering the new usages for existing drugs. We believe that this approach can be further applied to understand molecular binding mechanisms and take a new direction on drug discovery.To address this issue, we proposed a new concept of "Homopharma" to describe similar binding environments and the relationships of interactions between proteins and compounds. A Homopharma is a set of proteins which have the conserved sub-binding environment at the protein-compound interfaces and a set of compounds with similar topology. Our results demonstrated that protein-compound complexes of a Homopharma perform similar protein-compound interactions and comprise conserved specific residues and important functional sites. According to the Homopharma concept, four similar flavonoid derivatives were tested against 32 human protein kinases using In this study, we propose a new concept "Homopharma" to describe similar binding environment and the relationships of interactions between proteins and compounds. Proteins sharing similar binding environments can usually be targeted by a set of compound with similar topology. We considered that a Homopharma comprises of a set of proteins which have the conserved sub-binding environment at the protein-compound interfaces and a set of compounds with similar topology. For a complex structure, the proteins with similar pharma-interfaces are first recognized by SRPmotif. The complexes with significant compound topology similarity are reserved. Then, the protein-compound interaction similarity scores are measured. Consequently, the proteins and compounds which share similar protein-compound interactions are considered as a Homopharma. Figure SRPmotif is utilized to rapidly search potential targets having similar pharma-interfaces Figure . A pharmiGEMDOCK [A compound would bind to proteins having similar binding environments; as well as proteins often interact with compounds of similar topology. These protein-compound interactions provide the opportunities to explore the molecular binding mechanisms. After identifications of candidate complexes, similarities between all crystal ligands of complexes are calculated by atom pairs (AP) . The striGEMDOCK . DiffereAfter estimations of the protein-compound interaction profile, conserved interaction similarity scores between protein-compound interactions are evaluated. To measure the interacting conservation, the conservation weight of each position of multiple sequence alignment (MSA) is given as:i interacting with compounds with the interaction type k . The interaction similarity score between two protein-compound interactions (A and B) is defined as following:where N is the number of compound members of the pharma-interface and i Aand i Wis the interaction and the conservation weight in position i of the MSA, respectively.where Consequently, the protein-compound interactions are clustered by two-way hierarchical clustering. These interactions are divided into several Homopharma groups, which consist of similar binding environment, compound topology and interactions.To verify the Homopharma concept, protein-compound complexes were obtained from PDB. The complexes are eliminated when compounds are metal ions, surface compounds, cofactors, and small compounds (heavy atoms < 6). Two protein-compound pairs are grouped according to the same proteins or compounds. The protein-compound groups are filtered by number of complexes below 50% of all possible protein-compound combinations. Two groups which overlap with more than 50% protein or compounds are merged. Finally, 176 protein-compound groups that contain 1,325 complex structures including 672 proteins and 471 compounds are used as validation dataset.A protein-compound complex having significant compound similarity is considered as a candidate of a Homopharma. In order to decide the threshold of compound topology similarity, we collected 3,151 crystal complex structures of 957 proteins. The distribution demonstrates the relationships between percentage and compound topology similarity  curves were estimated to compare protein similarity, compound similarity and interaction similarity  complex  as the query complex. Due to specific expression of herpes thymidine kinase (TK) in herpesvirus-infection cells, TK becomes an antiviral target of current drugs, such as acyclovir and vidarabine. The identified pharma-interface contains five conserved pharma-motifs. Furthermore, the clustering of interaction profiles demonstrates protein-compound interactions can be separated into four Homopharma groups.The proteins and compounds in a Homopharma would share conserved interactions and similar physicochemical properties. Therefore, these compounds are often able to inhibit these proteins in a Homopharma. Figure The complexes in a Homopharma present similar binding modes and have the conserved interacting residues Figures  and 3C. Flavonoids are a class of plant secondary metabolites and have more than 5,000 kinds of derivatives. Flavonoid derivatives have been crystallized within more than 300 protein complex structures, such as protein kinases. Protein kinases play important roles in cell growth and signal transduction ,40. ProtIn this study, we use serine/threonine-protein kinase Pim-1 (PIM1) and quercetin complex  to identify pharma-interface and Homopharma groups. After recognitions of 975 protein-compound complexes by four pharma-motifs, six Homopharma groups are identified according to the interaction similarity Figures . The conHomopharma 4 also performs the conserved interactions to hinge motif Figure . HoweveriGEMDOCK; (2) interaction profiles were analyzed from the docking poses that have the lowest energy; (3) interaction similarity scores were measured to the PIM1-quercetin complex; (4) the kinases of the same Homopharma group have the priority with selection. Most kinases were selected from Homopharma 2, 4, and 5. Furthermore, some kinases which are either not considered potential targets or not searched by pharma-interface were also collected. Eventually, 32 protein kinases were chosen  using the "HotSpot" assay platform [These 32 tested kinases were divided into two Homopharma groups and the superimposed structures of two groups are presented in Figure 50 ≤ 10 μM) by in vitro enzymatic profiling. Among these 56 inhibitions, the IC50 values of 25 bioassays are less than 1 μM and these flavonoids can be considered as potential anticancer compounds. These results show that Homopharma is not only useful to identify potential targets of compounds, but also can reveal the key binding environments. We believe that the Homopharma concept is useful for understanding molecular binding mechanisms and providing new opportunities for drug repurposing.In this study, we propose a new concept of "Homopharma", combining similar binding environments and protein-compound interaction profiles, to explore the molecular binding mechanisms and drug repurposing. A Homopharma consists of a set of proteins sharing conserved binding environment and a set of compounds which have similar structures and functional groups. In a Homopharma, the compounds are often able to inhibit these proteins because their interactions and physicochemical properties are often consensus. Experimental results show that protein-compound complexes of a Homopharma often perform similar interactions in which formed by conserved binding residues (protein sites) and similar important functional groups (compound sites). According to the Homopharma concept, we successfully discovered 56 flavonoid-kinase inhibitions (ICThe authors declare that they have no competing interests.YYC, CTL, and JMY conceived and designed the experiments. YYC, JHT, KHL, and JMY implemented the materials/analysis programs. YYC, JHT, KHL, CTL, KCH, and JMY performed the experiments and analysed the data. YYC, JHT, KHL, CTL, and JMY wrote the paper.Click here for file


KinetochoreDB is an online resource for the kinetochore and its related proteins. It provides comprehensive annotations on 1554 related protein entries in terms of their amino acid sequence, protein domain context, protein 3D structure, predicted intrinsically disordered region, protein–protein interaction, post-translational modification site, functional domain and key metabolic/signaling pathways, integrating several public databases, computational annotations and experimental results. KinetochoreDB provides interactive and customizable search and data display functions that allow users to interrogate the database in an efficient and user-friendly manner. It uses PSI-BLAST searches to retrieve the homologs of all entries and generate multiple sequence alignments that contain important evolutionary information. This knowledgebase also provides annotations of single point mutations for entries with respect to their pathogenicity, which may be useful for generation of new hypotheses on their functions, as well as follow-up studies of human diseases.Database URL:http://lightning.med.monash.edu/kinetochoreDB2/ During cell mitosis and meiosis, the kinetochore plays a critical role of locating the attachments on chromosomes and pulling sister chromatids apart. Assembled on centromeric chromatin, kinetochore functions during the cell cycle . During Despite its biological significance and our increasing awareness of its potential roles in human diseases, there is currently a paucity of publically available databases or resources that focus on providing comprehensive functional annotations of the kinetochore and its related proteins. The only available database is MiCroKiTS , an inteIn an effort to address this knowledge gap, we created KinetochoreDB, which integrates several public databases, computational annotations and experimental results for currently 1554 related entries. KinetochoreDB contains several important features, the majority of which are not available in MiCroKiTS :Table 1It provides annotations of protein 3D structure when structural information is available. For protein entries with available structural information, the corresponding Protein Data Bank (PDB) IDs and their related information are provided. In addition, information on predicted intrinsic disorder is provided, which is particularly important for providing structural insights into those entries in KinetochoreDB whose 3D structures have not been solved.It provides comprehensive annotations of single point mutations and their pathogenic effects. These mutations are classified as either pathogenic or nonsense mutations in KinetochoreDB. For disease-associated pathogenic mutations, KinetochoreDB provides details of the disease caused by the mutation, allows users to search the entire database with the disease name of interest, and provides user-friendly options to browse the related kinetochore proteins that harbor such disease-associated mutations.It provides metabolic/signaling pathway information for each entry by cross-referencing the KEGG database. Such information is important for understanding the functions of kinetochore proteins from a biochemical network perspective. In particular, the pathway information and the link to Kyoto Encyclopedia of Genes and Genomes (KEGG) will be provided if an entry has pathway information available in KEGG.It provides multiple sequence alignments (MSAs) for all included entries, thereby allowing users to readily identify evolutionarily conserved regions within the family of a kinetochore protein. In addition, the visualization of MSAs implemented by Jalview is user-friendly and customizable.It provides convenient user enquiry and new entry submission options by enabling users to automatically upload their newly discovered sequences into the online database.Supplementary Table S1). In addition, we queried ‘subcellular location’ with the keyword ‘kinetochore’ in UniProt and downloaded the entries with published experimental evidence from the search results. After the removal of redundant entries, the resulting dataset contained a total of 1554 carefully reviewed entries. The detailed procedures of database construction and data collection are illustrated in We define ‘kinetochore and its related proteins’ with respect to protein subcellular location and Gene Ontology (GO). The entries of KinetochoreDB originate from three major resources; QuickGo database , UniProthttp://www.ncbi.nlm.nih.gov/gene/), 1000 Genome Project database  and pViz (http://andreasprlic.github.io/proteinfeatureview/), was used to display protein structural, functional domains and sites in a better and more interactive way. The display of protein domain context can be found in ‘Protein Domain Context’ section of the webpage. The information stored in KinetochoreDB resides in a MySQL relational database. A highly interactive web front-end to the data was implemented using the Javascript framework, JQuery. Apache Tomcat handles serving of data to users on the web, utilizing a set of Java Servlets and JavaServer Pages for data searching and viewing.We used Jmol (and pViz  for visuand pViz  for custhttp://lightning.med.monash.edu/kinetochoreDB2/Search.jsp) (The ‘Search’ page (rch.jsp)  allows urch.jsp) . After sSupplementary Figure S1A), allowing a more detailed inspection of the constituent domains of the protein. For protein structure overview, KinetochoreDB provides two different ways to inspect the 3D structures. A single structure for the current protein entry can be examined by clicking the ‘View’ button to launch Jmol, a Java applet for displaying 3D structures . Multiple structures can also be viewed together as an ensemble using pViz .For protein overview and domain context, KinetochoreDB uses The Protein Feature View plug-in to facilitate the general description of protein entries including functional sites and domains , certain proteins in MSAs might not be included in the current KinetochoreDB. To facilitate the comparison between entries in KinetochoreDB and their homologs, we archived the homologs by extracting protein UniProt IDs. Detailed information for these files is available in the ‘Protein alignment’ section of the webpage.Supplementary Figure S2). After careful review and validation, new data will be included in KinetochoreDB and made publically available.KinetochoreDB will be updated on a regular basis  to include newly available entries from various resources, in order to allow an up-to-date archive of recent results of the kinetochore and its related proteins. All the updates will be carefully reviewed prior to their release. In addition, we also allow users to submit their research findings related to kinetochore to our KinetochoreDB. New submission should include protein/gene detailed information , protein structural information, protein functional annotation, single point mutation and its pathogenicity and metabolic/signaling pathway  mutations were found to cause diseases, while 735 (51.6%) were nonsense mutations . PTMs, oIn addition, with the comprehensive dataset from KinetochoreDB, we conducted a statistical analysis of the number of proteins involved in different GO terms including cellular component, molecular function and biological process. The results are shown in It should be noted that the statistical results regarding the PTM sites, mutations and GO terms are merely observations based on the entries in KinetochoreDB, rather than being interpreted as a biologically siginificant finding, as the entries included in KinetochoreDB only represent a subset of the entire proteome and are from different species.The kinetochore and its related proteins play extremely important roles during cell division and mitosis. In the past few decades, research on this topic has attracted a great deal of interest, not only because they are important for the cell cycle, mitosis and meiosis , but alsIn the future, we will endeavor to improve and update the annotations and analysis of data entries in KinetochoreDB by the following means: (i) We will keep the database updated and provide up-to-date information to synchronize with the research progress in the kinetochore and its related proteins; (ii) We will integrate genomic information into our database and source these data from publicly available information or bioinformatics programs. These include coding sequence, transcription factor binding site, enhancer, promoter and other upstream or downstream regulatory information; (iii) We will combine other state-of-the-art predictors to annotate the natively disordered regions of all entries in the database, while highlighting the consensus prediction. Meanwhile, we will also collect experimentally verified disordered regions from DisProt , the mosIn addition we will continue to improve and update the annotations and analysis of all entries in KinetochoreDB by implementing secondary analysis functions of the database and by integrating high-throughput experimental data. In particular, we will explore gene expression microarray data, transcriptomics and proteomics and detailed functional pathway data, so as to provide a comprehensive useful resource for the wider research community.Supplementary data are available at Database Online.


Protein ubiquitination catalyzed by E3 ubiquitin ligases play important modulatory roles in various biological processes. With the emergence of high-throughput mass spectrometry technology, the proteomics research community embraced the development of numerous experimental methods for the determination of ubiquitination sites. The result is an accumulation of ubiquitinome data, coupled with a lack of available resources for investigating the regulatory networks among E3 ligases and ubiquitinated proteins. In this study, by integrating existing ubiquitinome data, experimentally validated E3 ligases and established protein-protein interactions, we have devised a strategy to construct a comprehensive map of protein ubiquitination networks.In total, 41,392 experimentally verified ubiquitination sites from 12,786 ubiquitinated proteins of humans have been obtained for this study. Additional 494 E3 ligases along with 1220 functional annotations and 28588 protein domains were manually curated. To characterize the regulatory networks among E3 ligases and ubiquitinated proteins, a well-established network viewer was utilized for the exploration of ubiquitination networks from 40892 protein-protein interactions. The effectiveness of the proposed approach was demonstrated in a case study examining E3 ligases involved in the ubiquitination of tumor suppressor p53. In addition to Mdm2, a known regulator of p53, the investigation also revealed other potential E3 ligases that may participate in the ubiquitination of p53.Aside from the ability to facilitate comprehensive investigations of protein ubiquitination networks, by integrating information regarding protein-protein interactions and substrate specificities, the proposed method could discover potential E3 ligases for ubiquitinated proteins. Our strategy presents an efficient means for the preliminary screen of ubiquitination networks and overcomes the challenge as a result of limited knowledge about E3 ligase-regulated ubiquitination.The online version of this article (doi:10.1186/s12918-015-0244-1) contains supplementary material, which is available to authorized users. Protein ubiquitination involves a series of enzymatic reactions such as E1 activation, E2 conjugation, and E3 ligation, resulting in the conjugation of single or multiple ubiquitin proteins at a target lysine residue . IdentifTo date, a significant amount of research efforts have been invested towards the characterization of E3 structures and examination of the mechanisms underlying E3-mediated regulatory networks, as well as E3-related diseases –21. Base. [Many databases and tools have been developed to aid in the study of E3 ligases. For example, E3Miner  offers a.  construcConstruction of the protein ubiquitination networks involved collection of E3 ligase and ubiquitinated protein data, integration of ubiquitinated proteins’ functional data, computational identification of ubiquitination sites based on substrate motifs, as well as network construction using protein-protein interactions and metabolic pathways Fig. . A netwoExperimentally validated E1 activating, E2 conjugating, and E3 ligating enzyme data were obtained from various sources. From UUCD-Version 1.0 , seven dS-nitrosylation [S-glutathionylation [To characterize the amino acid composition of protein ubiquitination sites, WebLogo , 34 was sylation , O-GlcNAsylation , S-glutanylation , as wellnylation .To investigate the biological significance of ubiquitinated proteins, various biological databases, such as Gene Ontology (GO) , InterPrSubstantial evidence supports the role that protein ubiquitination plays in the regulation of cellular processes. Thus, by integrating experimentally validated mammalian E3 ubiquitin ligases and their functional information, we hoped to provide a foundation for navigating ubiquitination regulatory networks in mammals. To facilitate the exploration of regulatory relationships between E3 ligases and their ubiquitinated substrates, associated metabolic pathways and protein-protein interactions (PPIs) were included for the comprehensive construction of protein ubiquitination networks. The human metabolic pathways were extracted from KEGG . ExperimG =  to symbolize a protein ubiquitination network, where x , y ∈ V and  ∈ E. The E3 ligases and substrate proteins were represented by x and y, respectively, and protein ubiquitination was denoted by  ∈ E to indicate the recognition of a specific substrate y by E3 ligase x  could also represent a type of protein-protein interaction between E3 ligase x and ubiquitinated protein y. We used V to refer to all human proteins and E, to all experimentally confirmed PPIs. Cytoscape [Next, a graph theory , 57 apprytoscape , a publi, potential substrates of E3 ligases could be inferred from the 27,227 PPIs between E3 ligases and other proteins. Moreover, 377,117 PPIs that appeared to involve ubiquitinated proteins could be integrated for the investigation of their functional associations in the context of ubiquitination.Data used for building the protein ubiquitination networks in this study were experimentally validated and supported with 39,814 research articles. Over 500 research articles were manually reviewed via a text mining method. In total, 41,392 ubiquitination sites from 12,786 ubiquitinated proteins in humans were extracted from 406 literatures. After removing redundant data among heterogeneous online resources, 494 experimentally verified human E3 ubiquitin ligases remained in the resulting data. PPIs between E3 ligases and ubiquitinated proteins were retrieved to deduce potential regulatory relationships between E3 ligases and ubiquitinated substrates to compensate for the limited information about E3 ligase targets. As shown in Table p < 0.005).The entropy plots generated by the sequence logo was used to graphically visualize the amino acid sequences flanking the substrate sites (at position 0). This allows for the easy observation of amino acid conservation surrounding the ubiquitination sites. Figure To overcome the difficulty of discovering conserved motifs from large-scale ubiquitinome data, the MDDLogo clustering method was adopted to search for substrate motifs from the curated human ubiquitination sites using a 13-mer window length. MDDLogo identified a total of nine subgroups containing conserved motifs from non-homologous human ubiquitination sites (Additional file Distributions of GO annotations for E3 ligases and ubiquitinated proteins categorized according to their corresponding biological processes, molecular functions and cellular components are provided in (Additional file According to the annotation information on InterPro, approximately 70 % of established ubiquitination sites are mapped to specific functional domains, suggesting that ubiquitination may modulate a variety of biological functions. The top 50 InterPro functional domains containing ubiquitinated sites in humans are given in Table To allow users to efficiently search for the proteins of their interest, a convenient interactive network viewer was implemented in the proposed method implemented an interactive network viewer. An example of constructing a protein ubiquitination network using our approach is illustrated in (Additional file In cases where information is limited with respect to the interaction between an E3 ligase and its corresponding substrates, our strategy could still identify the potential E3 ligases that may target a specific ubiquitinated protein. A case study is shown in Fig. In an attempt to characterize the regulatory role protein ubiquitination plays in a variety of biological processes, we combined the information of E3 ligases, ubiquitinated proteins, and protein-protein interactions to construct a comprehensive network of E3 ligases and their ubiquitinated substrates. Designed to serve as not only a meaningful platform for investigating E3-substrate regulatory networks but also a new strategy to uncover potential E3 ligases for ubiquitinated substrates, the proposed approach allows for the efficient characterization of protein ubiquitination networks from large-scale ubiquitinome data. With access to more updated data, the proposed scheme can be further refined for the study of E1 activating enzymes, E2 conjugating enzymes, and E3 ubiquitin ligases. Also, recent publications regarding the structural environment of experimentally validated ubiquitination sites based on protein tertiary structures –76 could


Native proteins perform an amazing variety of biochemical functions, including enzymatic catalysis, and can engage in protein-protein and protein-DNA interactions that are essential for life. A key question is how special are these functional properties of proteins. Are they extremely rare, or are they an intrinsic feature? Comparison to the properties of compact conformations of artificially generated compact protein structures selected for thermodynamic stability but not any type of function, the artificial (ART) protein library, demonstrates that a remarkable number of the properties of native-like proteins are recapitulated. These include the complete set of small molecule ligand-binding pockets and most protein-protein interfaces. ART structures are predicted to be capable of weakly binding metabolites and cover a significant fraction of metabolic pathways, with the most enriched pathways including ancient ones such as glycolysis. Native-like active sites are also found in ART proteins. A small fraction of ART proteins are predicted to have strong protein-protein and protein-DNA interactions. Overall, it appears that biochemical function is an intrinsic feature of proteins which nature has significantly optimized during evolution. These studies raise questions as to the relative roles of specificity and promiscuity in the biochemical function and control of cells that need investigation. When one looks at the diversity of protein functions, one cannot but wonder how they came about. At first glance, the natural tendency is to assume that their remarkable properties mainly arise from evolutionary selection, with the inherent background features that reflect the physical properties of proteins playing a minor role. If so, proteins should exhibit little intrinsic background function, and those that do should be very rare8. The fundamental problem with this viewpoint is that for selection to occur, there must be some background function on which to select; in practice, low-level function emerges remarkably quickly in function design studies11. The key issue is how to estimate this random background probability for function. Here, computer experiments can provide important insights16. For function to occur, often there must be an interaction between molecules. Thus, in what follows, we examine the inherent ability of proteins to engage in small molecule protein-protein and protein-DNA interactions. Surprisingly many biochemical properties of native proteins are found in a library of stable artificial structures generated without any selection for biochemical function. Remarkably, this includes enzymatic active sites and, at much lower frequency, pockets that loosely resemble the enzymatic binding pocket. This suggests that functional selection by evolution is most likely involved in fine-tuning rather than in generation of intrinsic function. If so, marginally stable proteins are inherently ready to engage at low level in the biochemical functions necessary for life.Often proteins adopt a unique, thermodynamically stable native conformation that can perform an amazing variety of biochemical functions ranging from enzyme catalysis and signal transduction to force generation19. To cover all representative protein functions would be a long, expensive process that is, at present, impractical. Rather, we chose to perform a series of computer experiments16, where a library of compact homopolypeptides from 40–250 residues in length were generated using the TASSER structure prediction algorithm20. Then, sequences with protein-like composition were selected by optimizing their thermodynamic stability in the putative fold of interest13. These artificial proteins are termed the “ART” protein library.To separate out the intrinsic properties of proteins from those due to evolution, one could design proteins without selection for function, solve their structures, assay their function, and explore their similarity to native proteins21, is likely complete and arises from defects in packing of compact secondary structures, as proteins without secondary structure have tiny pockets that cannot bind biologically relevant molecules22. In practice, for single-domain globular proteins, the space of protein pockets is covered by a remarkably small number (about 500) of representative pockets. These results are consistent with a large-scale study on a non-redundant set of ~20,000 known ligand-binding pockets that finds their structural space is crowded, likely complete, and represented by a similar number of pockets23. Similar protein pockets occur in proteins that have globally unrelated folds. On the other hand, closely related proteins need not have similar pockets. The presence of similar pockets capable of binding similar, if not identical, ligands in multiple protein families rationalizes at least part of the reason why drugs have unintended side effects.Having the ART library in hand, we compared the small molecule ligand-binding pockets to those in native proteins. Remarkably, all ligand-binding pockets in native proteins have a statistically significant match to the pockets in the ART library. This suggests that the library of all ligand-binding pockets, the “pocketome”24) were screened against a representative set of ART proteins using the FINDSITEcomb virtual ligand screening algorithm25. FINDSITEcomb has an average success rate of 21% at identifying micromolar or better binders when 50 or fewer small molecules are screened26. Enrichment factors of the top 1% of ranked ligands relative to a set of 69,271 background molecules (the ZINC 8 library27) culled with a TC28 of 0.7 were 2.57, with 98.6% of ligands having an enrichment factor >1 (the random background result). We found that the median number of binding targets per KEGG molecule is 35, quite close to the number (38) of proteins predicted to bind to drugs in the human exome29. Of these 1400 molecules, 1186 or 84.7% molecules have at least one binding target, and the median number of small molecules that bind per protein is 36 .A representative set of 1400 Kyoto Encyclopedia of Genes and Genomes (KEGG) molecules 35. In total, we studied 1373 protein chains that are annotated as being enzymes. For each target enzyme, we first detected pockets using a geometry-based method36. We then scanned these pockets against known active sites of the template library of enzymes37. If the target had an amino acid arrangement with a similar geometry as the active sites of a template enzyme whose root-mean-square-deviation (RMSD) from that of the known enzyme’s active site <1 Å RMSD and had 100% sequence identity, we considered it a hit. About 94% of the enzymes hit at least one template enzyme that had different first two-digit Enzyme Commission (EC) numbers, i.e. they are from very different enzyme classes. We further counted hits according to their enzyme classes at the four-digit EC level using various RMSD cutoffs  databasecutoffs ; 75% of 23 and a set of the same number of randomly selected artificial structures, we first identified the largest pocket in these structures, then searched in these pockets for residues that resemble active sites in native enzymes. We only considered hits that had a different global structure with a template modeling (TM)-score <0.438  from a target native enzyme. Using the same criteria, at an RMSD <1 Å and 100% coverage and sequence identity, we found at least a hit for 35% of enzyme active sites in non-enzymes and a comparable value (37%) in artificial structures. For an RMSD <0.75 Å, 29% and 31% of native active sites were matched, respectively. Finally, at an RMSD <0.50 Å, 25% and 26% of native active sites were found for non-enzymes and artificial structures, respectively. Small-size active sites were mostly easy to find a hit: about 88% of three-residue active sites, 35% of four-residue active sites, and 0.3% of five-residue active sites were found in artificial structures. About 25% of enzymes had more than four hits in artificial structures. However, it should be pointed out that the global pockets in these matches usually did not have a significant similarity score to the native active site pocket, despite the high structural similarity of their active site residues. Whether these native non-enzymes could weakly catalyze a similar reaction in a different substrate is unknown, as there are other factors that could dictate enzymatic activity39. To further investigate this issue, we froze the catalytic residues in the artificial structure of interest and generated sets of stable sequences for the given fold. We then examined whether artificial pockets globally similar to the active pocket in that native enzyme are generated. As shown in40, one need only sample on the order of ~104–105 random sequences to generate a pocket that is at least weakly related to the native pocket. For these, the RMSDs of the aligned residues versus the number of aligned pocket residues for eight pairs of native enzymes-ART proteins are shown in37. At this range of PS-scores23, about 13% of ligands share significant chemical similarity as assessed by their TC28.Next, we performed a search of enzyme-like active sites in native structures of non-enzymes  and in t15. Interestingly, they also possess the ability to form native-like protein-protein and protein-DNA complexes. To demonstrate this, we randomly selected 30,000 pairs of ART structures in representative native-like folds; each fold had 80 protein-like sequences predicted to be stable for that fold. This gives 192 million pairs of ART monomers. To find possible native-like complexes, a simple yet efficient strategy was adopted. First, we compared the backbone structural similarity of ART monomeric structures with monomeric structures found in a library of 1690 non-redundant native dimeric complexes42. Using structural alignments, we built putative complexes by superimposing individual ART structures onto their corresponding aligned monomers from the native templates38. We only considered those putative complexes that had significant global structural similarity and were aligned to more than 50% of the native interface. This yielded 135,942 putative ART complexes, and each had a corresponding native protein complex as its template. As shown in-5 of the total, had strongly favorable interactions and shared significant structural similarity with their templates. These ART complexes may be considered native-like. In general, attractive ART interfaces are enriched in hydrophobic residues. The protein-binding propensity scores of attractive ART complexes overlap with the scores of native complexes (43 and shares significant interface similarity (IS) at an IS-score p-value of 7×10-4 with respect to the closest native protein complex44. Thus, putative native-like protein-protein complexes are found without any selection whatsoever for protein-protein interactions.Not only do the ART structures resemble native proteins in terms of fold similarity and ligand-binding pocket but docked ART structures match native protein-protein interfaces, suggesting that the space of protein-protein interfaces is complete and covered by roughly 1000 distinct types of interfacesmplexes . An exam45. The vast majority had either energetically unfavorable DNA-protein interfaces or did not share significant structural similarity with their corresponding native protein templates . Thus, active site geometry is not special, and it appears that a significant fraction of the biochemistry of life, at least at very low level, is encoded in the physical properties of proteins. If this view is true, and these observations need to be experimentally validated, this has significant implications for the origin of life.22. Again, there is the implication of weak omnipresent promiscuous interactions in a cell. How cells sort out the myriad of weak interactions relative to the small fraction of specific ones needs to be better clarified. Part of the answer may lie in subcellular localization.Turning to the likelihood of protein-protein and protein-DNA interactions occurring at random, the strong implication is that a tiny fraction of proteins can engage in at least intermolecular interactions without functional selection. Once again, intermolecular interactions emerge as an inherent feature of proteins due to the packing of secondary structuresOverall, these studies suggest that the “special” functional properties of proteins are not as special as commonly viewed. Pockets, enzymatic active sites, and native-like protein-protein and protein-DNA interactions are found in artificial protein structures that are selected for stability and nothing more. The packing of secondary structure is found to provide the geometric context for pockets and intermolecular interfaces. The requirements that a protein be compact and water soluble and adopt a thermodynamically unique conformation give rise to protein sequences that recapitulate the necessary functional features (at least at low level) of real native proteins. Overall, it appears that biochemical function is merely an intrinsic feature of proteins that nature has then significantly optimized.