Identification of protein complexes in large interaction networks is crucial to understand principles of cellular organization and predict protein functions, which is one of the most important issues in the post-genomic era. Each protein might be subordinate multiple protein complexes in the real protein-protein interaction networks. Identifying overlapping protein complexes from protein-protein interaction networks is a considerable research topic.k-clique community of algorithm CPM and introduced distance restriction, and develop a novel algorithm called CP-DR based on the new topological model for identifying protein complexes. In this new algorithm, the protein complex size is restricted by distance constraint to conquer the shortcomings of algorithm CPM. The algorithm CP-DR is applied to the protein interaction network of Sacchromyces cerevisiae and identifies many well known complexes.As an effective algorithm in identifying overlapping module structures, clique percolation method (CPM) has a wide range of application in social networks and biological networks. However, the recognition accuracy of algorithm CPM is lowly. Furthermore, algorithm CPM is unfit to identifying protein complexes with meso-scale when it applied in protein-protein interaction networks. In this paper, we propose a new topological model by extending the definition of The proposed algorithm CP-DR based on clique percolation and distance restriction makes it possible to identify dense subgraphs in protein interaction networks, a large number of which correspond to known protein complexes. Compared to algorithm CPM, algorithm CP-DR has more outstanding performance. With the Human Genome Project implement successfully, the biomedical research enters the post-genome era. In the new era, one of the most important challenges is to systematically analyze and comprehensively understand how the proteins accomplish the life activities by interacting with each other . It playet al. proposed the Restricted Neighborhood Search Clustering (RNSC) algorithm which aimed at exploring the best partition of networks by using a cost function [et al. proposed a fast agglomerative algorithm based on greedy strategy [et al gave a new self-contained algorithm [et al developed an agglomerative algorithm MoNet [The basic idea of Non-overlapping Clustering Algorithms is that each protein belongs to one and only one protein complex in large-scale protein-protein interaction network. King function . In addifunction , Girvan function , Newman strategy . In recelgorithm  and Luo hm MoNet  and so ohm MoNet . Therefohm MoNet .et al.[3)O , the betet al. discusselgorithm , Zhang eLi et al. developeet al., named Clique Percolation Method (CPM) [k. The smaller values of k correspond to the more excessive large subgraphs of high density. In order to conquest these shortcomings, an algorithm called CP-DR (Clique Percolation Method based on Distance Restriction) for identifying protein complexes based on clique percolation and distance restriction is proposed in this paper. In this algorithm, the scale of protein complex is restricted by distance constraint. The experiment results show that algorithm CP-DR can detect a large number of protein complexes with specific biological significance and biological functions more effectively, more precisely and more comprehensively.With specialized research in the overlapping structure of large-scale complex network, a powerful algorithm for finding protein complexes and exploring the general characteristics of complex networks in biology based on clique percolation has been recently developed by Palla od (CPM) . In addiod (CPM) . As an eod (CPM) ,16, whilet al. proposed the algorithm CPM based on clique percolation method [k-clique community which was defined as the union of all k-cliques (complete subgraphs of size k) that can be reached from each other through a series of adjacent k-cliques (where adjacency means sharing k-1 vertices). The k-clique community can be considered as a usual module  because of its dense internal links and sparse external linkage with other part of the whole network. All of k-cliques of network can be received by iterative recursion, then construct the overlap matrix of these k-cliques. Finally, a number of k-clique communities are discovered by analysis the overlap matrix. As a simple illustration, Fig.k-clique-communities at k = 4 using the clique-clique overlap matrix.Palla n method . The undk. Generally speaking, the larger value of k chose, the smaller size of k-clique communities of higher density would be obtained. And it is no doubt that vertices are relatively dense linked internal each k-clique community. Although the algorithm CPM analysis the overlapping modular structure of society and biology is effective, the drawback is protein complexes quantity identified by this algorithm limited. Especially, protein complexes quantity is fewer when the relatively large k value chose. The large-scale k-clique communities usually correspond to small k values, that is to say, the smaller value of parameter k selected, the larger size of k-clique communities have. We applied the CPM method to yeast protein-protein interaction network and detected interesting protein complexes which might overlap each other. When using k=4, taking into account the basic topological unit as 4-clique, there is a large identified protein complex containing 348 vertices and 2499 pairs interactions. Meanwhile, there is an excessive huge protein complex detected with 865 vertices and 4508 pairs interactions when choosing k=3. As the previous examples, the scale of k-clique communities is far greater than scales of k-cliques and sparse k-clique chain.As is known to all, the result of algorithm CPM associated closely with the value of clique percolation parameter k-clique communities identified by algorithm CPM could be decomposed into multiple relatively dense subgraphs as the protein complexes with some special biological significance more effectively, more precisely and more comprehensively. In Fig.k=3. In real protein-protein interaction networks, however, there should be three small overlapping protein complexes  ,19. Therxes Fig. .k-clique community is considered as a protein complex because of its dense internal links and sparse external linkage with other part of the protein-protein interaction networks. According to the relational biological characteristics and topological properties, the CPM method should be improved to identify protein complexes with more advantages such as meso-scale, high accuracy, more effectively and more comprehensively. In our approach, in order to achieve these advantages, we propose a new topological model by extending the definition of k-clique community of algorithm CPM and introduced distance restriction. Therefore, the scale of clusters identified by our approach is restricted by distance constraint and protein complexes considered as clusters satisfying distance limits.In algorithm CPM, each N vertices). In this definition, the distance is represented by the diameter of the identified cluster .Our new topological structure of identified clusters is based on the observation that a typical member in a cluster is linked to many other members, but not necessary to all other vertices in the cluster. In other words, our new topological structure of identified cluster can be interpreted as a union of small complete (fully connected) subgraphs that share vertices. We could definition the identified cluster as the union of all maximal cliques that satisfying the distance restriction and that can be reached from each other through a series of adjacent maximal clique C the number of common vertices between basic cluster units U and V, by lC the largest length of a length of shortest path between a pair of vertices in the union of U and V. Because of our new topological model based on this two condition cC and l,C our discussion will be mainly focused on them.In the following discussion, we donate by N vertices). This condition can be depicted by the following formula:In the definition of our new topological model, the identified cluster could be seen as the union of all maximal cliques that can be reached from each other through a series of adjacent maximal clique  ≥ N        (1)N represents the common vertices between basic cluster units U and V.where In our new topological model, the identified cluster also should be satisfying the distance restriction. As mentioned above, the distance is represented by the diameter of the identified cluster. According to the small-world property of the protein interaction networks ,28, thisCl ≤ d        (2)d represents the diameter of the identified cluster.where k-cliques will be merging if and only if they share k-1 vertices in algorithm CPM. According to this underlying idea, it is obviously that the larger value of the clique percolation parameter k chose, the more difficult k-cliques merge. The vast majority of predicted clusters are sole k-cliques. By the experimental analysis, the predicted clusters identified by this extending rule with large value of k are inefficient and incomplete. Reversely, the smaller value of k chose, the easier k-cliques merge and the scale of predicted clusters will be huger. That is why we obtain excessive huge clusters with 865 vertices and 4508 pair interactions in algorithm CPM when using the clique percolation parameter k=3. Generally speaking, the predicted clusters identified by this extending rule with small value of k are excessive large and low accuracy. In order to overcome this shortcoming of algorithm CPM, we define the novel extending rule as two maximal cliques will merge if they share N vertices. The number of common vertices N defined by the following formula:It is known to all from previous subsection that two N = MIN – 1        (3)|U| and |V| are the size of basic cluster unit U and V.where et al. analyzing the topology of complex in the protein interaction network of Saccharomyces cerevisiae. Of the 216 gold standard protein complexes, 118 are connected (a protein complex is connected if there is a path connecting every pair of vertices in the complex). They have found that 94.91% of the connected complexes and 82.66% of the non-connected complexes have their diameter bounded 2. Furthermore, they found that 99.15% of the connected complexes and 93.88% of the non-connected complexes have their average shortest path length bound by 2. It is known to all this fact matches the observation that the protein interaction networks have the small-world property [d=2 as the distance restriction condition, that is to say, we restrict arbitrary pair of vertices in identified clusters absolutely no more than 2.In literature , Li et aproperty ,28. ThisAccording to the detailed depiction in characteristics of our new topological model, we propose a novel algorithm called CP-DR (Clique Percolation Method based on Distance Restriction) for identifying protein complexes based on clique percolation and distance restriction. The description of algorithm CP-DR is shown in Fig. G  with proteins as vertices and protein interactions as edges is created firstly. And then, we search all of maximal cliques with size no less than 3 in G. Next, each maximal clique is initialized as a basic cluster unit. In the basic clusters unit collection, if there are N common vertices between any pairs basic cluster units  and the union of  satisfying the condition absolutely no more than 2 when basic cluster unit U is not same as basic cluster unit V, we will save the union of . The first basic cluster unit will be deleted if and only if mergence appearance and all of comparison accomplishment. At last, all of identified clusters in S are exported.As shown in Fig.O(m). Enumerating all maximal cliques with size no less than 3 is a NP-complete problem, and only non-polynomial time algorithms for solving it are known. It has an upper bound of O(nmu) in step 2. In step 3 of algorithm CP-DR, each maximal clique initialized as a basic cluster unit is O(u) for time complexity. In the core step of algorithm CP-DR, the time complexity has an upper bound of O(2s3u). At last, the step of exporting all of identified clusters is O(u) for time complexity. This implies an upper bound of O(nmu+2s3u) for time complexity of algorithm CP-DR, where n is the number of nodes , m is the number of edges, u is the number of maximal cliques with no less than 3 in the graph and s is the size of largest maximal clique.In step 1 of algorithm CP-DR, the time complexity of protein-protein interaction information transformed into undirected simple graph is http://angel.elte.hu/clustering/. The protein interaction network of Saccharomyces cerevisiae is downloaded from MIPS (Munich Information Center for Protein Sequences) database. We remove all the self-connecting interactions and repeated interactions. The final network includes 4546 yeast proteins and 12319 interactions. The average clustering coefficient of the final network is 0.4, the network diameter is 13, and the average vertex distance is 4.42. We applied the proposed algorithm CP-DR to this network.To evaluate the suitability and validity of our proposed algorithm in identifying the overlapping protein complex in protein-protein interaction networks, we have used C++ language to implement algorithm CP-DR and download the overlapping protein complexes identification tool CFinder from sensitivity, specificity and f-measure of the algorithm CP-DR, calculate the overlapping rate of the predicted clusters, and evaluate the significance of the predicted clusters. We will also compare the algorithm CP-DR to the clique percolation method CPM in their performance on these measures.In the following subsections, we will compare the predicted clusters with the known complexes, analyze the Pc) matches a known complex (Kc). The overlapping score OS between a predicted overlapping structure Pc and a known complex Kc is calculated by the following formula:To evaluate the effectiveness of the algorithm CP-DR in detecting protein complexes, we compare the predicted overlapping structures produced by this algorithm with known protein complexes in MIPS yeast complex database. There are 216 manually annotated complexes considered as the gold standard data that each consists of two or more proteins. The largest complex contains 81 proteins, the smallest complex contains 2 proteins, and the average size of all the complexes is 6.31. Here, we use the same scoring scheme used in -20 to dei is the size of the interaction set of the predicted overlapping structure and the known complex, Pc||V is the size of the predicted overlapping structure and |KcV| is the size of the known complex.where Kc that has no proteins in a predicted overlapping structure Pc has OS = 0 and a known complex Kc that perfectly matches a predicted overlapping structure Pc has OS = 1. A known complex and a predicted overlapping structure are considered as a match if their overlapping score is equal to or larger than a specific threshold. Generally speaking, the more known complexes are matched by algorithm, the stronger identification ability algorithm has.A known complex k=3, 4, 5 are shown by CPM (k=3), CPM (k=4) and CPM (k=5) respectively. It is CP-DR represents that the matched known complexes identified by our approach. From this schematic diagram, we understand that the numbers of matched known complexes with different overlapping scores threshold for identified protein complexes generated by algorithm CP-DR is significantly higher than that identified by algorithm CPM with arbitrary values of clique percolation parameter k. We can obtain the following conclusion by analysis Fig.k chose, the stronger identification ability in detecting known complexes algorithm CPM has. However, the identification ability of algorithm CPM is still further inefficient than that of algorithm CP-DR. We get the largest number of matched known complexes detected by algorithm CPM when clique percolation parameter k=3. However, the matched known complexes quantity which identified with different overlapping score thresholds decreasing with the value of k increasing. The reason of this phenomenon is that the basic topological unit k-cliques are decrease with the values of k increase. And the number of identified protein complexes is changed by the basic topological unit k-cliques quantity. The same conclusions had been provided by Zhang and Jonsson via taking advantage of CFinder analysis of protein-protein interaction network [k= 3, 4, 5 respectively. By the introduction of distance restriction in algorithm CP-DR, the numbers of identified protein complexes is 2013, which is far greater than the identified protein complexes quantity by algorithm CPM with any values of clique percolation parameter k. As shown in Figure The numbers of matched known complexes with respect to different overlapping scores threshold (from 0 to 1 with a 0.1 increment) for result data sets generated by algorithm CPM using different parameter values and algorithm CP-DR are shown in Figure  network ,22. In ok=3 and algorithm CP-DR.In our experiment, we found that almost all of protein complexes identified by algorithm CPM could be accurately detected by algorithm CP-DR when protein complexes meeting the distance restriction condition. In addition, the introduction of distance restriction reasonable limits the size of protein complexes so that algorithm CP-DR could identify a large number of protein complexes with specific biological significance and biological functions more effectively, more precisely and more comprehensively. Table Complex 1 and Complex 2 are identified as an integral protein complex by algorithm CPM, but the real overlapping protein complexes could be detected by algorithm CP-DR and the matching extent are perfect.In Table k=3 is given in Fig.OS far less than 10-3. When we apply algorithm CP-DR to the same protein-protein networks, we indentify 1710 protein complexes which protein vertices and interactions included in the hugest protein complex identified by algorithm CPM. In Fig.OS≥0.2. In Fig.OS corresponding to 0.917, 0.559, 0.595, 1 respectively. In addition, we could obtain protein complexes 162, 348, 567, 689 respectively when the matching extent threshold OS respectively no less than 0.5, 0.4, 0.3, 0.2 in our experiment. The result illustrate that the complexes, which identified by distance-based constraint restriction, comparing to what detected by algorithm CPM are more accurately, more efficiently and more comprehensively.In our algorithm CP-DR, the size of protein complex is mainly restricted by distance constraint. It is precisely because of the introduction of distance restriction that the identified protein complexes with higher matching extent to known protein complexes and more prominent biological significance. A huge protein complex identified by algorithm CPM with clique percolation parameter Sensitivity and specificity are two important aspects to estimate the performance of algorithms in detecting protein complexes [Sensitivity is the fraction of the true-positive predictions out of all the true predictions, defined by the following formula:omplexes . SensitiTP (true positive) is the number of the predicted complexes matched by the known complexes with OS ≥0.2, and FN  is the number of the known complexes that are not matched by the predicted complexes. Specificity is the fraction of the true-positive predictions out of all the positive predictions, defined by the following formula:where FP  equals the total number of the predicted clusters minus TP. According to the assumption in [OS ≥0.2. Here, we also use 0.2 as the matched overlapping threshold.where ption in , a predif-measure, has been used in [Another integrated method, called the  used in , which ispecificity, sensitivity and comprehensive evaluation f-measure of algorithm CPM and algorithm CP-DR have been compared in Table The TP, which matched by the known complexes with OS ≥0.2 is significant greater than the unidentified complexes, FN, in the same threshold values. Therefore, there is a conclusion that the higher sensitivity correspond to lager TP and smaller FN. The conclusion also indicated that the detected complexes detected by algorithm CP-DR are greater reliability. The greatest sensitivity of algorithm CPM at arbitrary parameters is 0.213592233, only a quarter of sensitivity of algorithm CP-DR. As is well-known, Specificity is the fraction of the true-positive predictions out of all the positive predictions. The specificity of algorithm CP-DR is greater than 0.39, meanwhile, the specificity of algorithm CPM with arbitrary parameters k are greater than 0.2. Specifically, the algorithm CPM specificity is significant greater than that of algorithm CP-DR when the clique percolation parameter k ≥4. Though the specificity of algorithm CPM is higher than that of algorithm CP-DR, it is far less that the number of complexes identified by algorithm CPM compare to the known complexes quantity. In addition, because of the reference set MIPS is incomplete, some predicted complexes that may be true complexes could be regarded as false positives (FP) if they do not match with the known complexes. Nevertheless, it is still reasonable to consider a method more effective if it identifies more known complexes. From Table f-measure of algorithm CP-DR is more than twice the algorithm CPM. These results illustrate that the performance of algorithm CP-DR is more excellent in the protein complexes identification.As is known to all from Table G, the average occurrence times of each vertex v in all of induced subgraphs.Overlapping Rate: In undirected graph According to the definition, we calculate overlapping rate defined by the following formula:vk is the number of occurrences of each vertex v in all of predicted complexes, iN is the size of predicted protein complexes, and S is all of identified protein complexes collection.where Complex-Complex Interactions. The experiment result has been shown in Fig.OS greater than 0.8. We found that complex199 and complex211 are overlapping as the same as complex211 and complex216. However, complex207 and complex197 are isolate respectively.Since each protein might be involved in multiple biological processes in the real protein-protein interaction networks, that is to say it might belong to several protein complexes, it is necessary to decompose protein-protein interaction networks into overlapping nested structures. Moreover, many researches have proved that this measure is consistent with the practical situation. In our paper, the protein complex identified by algorithm CPM containing 685 vertices and 4508 pair interactions corresponds to 1710 protein complexes detected by our approach. In order to analyze the overlapping rate, we selected 58 members of 1710 protein complexes. According to the protein complexes existing overlap or not, we could construct By the analysis protein complexes detected by algorithm CP-DR and algorithm CPM, we found that a vast majority of proteins only subordinate one or two complex. The situation of three or more protein complexes contain a same protein is rare.k. When the clique percolation parameter k=3, the complexes overlapping rate is greatest, OR=1.192. Furthermore, the overlapping rate will decrease with the k increase. Finally, the overlapping rate will equal to one, that is to say, all of protein complexes are isolate respectively. The reason is that the basic topological unit k-cliques are decrease with the clique percolation parameter k increase. The number of predicted complex would reduce to one if the value of k increased to a certain extent. The overlapping rate is OR greater than 2.1 by analysis protein complexes predicted by algorithm CP-DR. Moreover, the rate of OR>1 of protein complexes predicted by algorithm CP-DR significant greater than that of algorithm CPM.Table P-value based on hypergeometric distribution is often used to estimate whether a given set of proteins is accumulated by chance. It has been used as criteria to assign each predicted complex a main function [P-value for each predicted complex and assign a function category to it when the minimum P-value occurs. The P-value is defied as follows [In order to detect the functional characteristics of the predicted complexes, we compare the predicted complexes with known functional classification. The function ,17. Here follows ,25:N is the total number of vertices in the network, C is the size of the predicted complex, F is the size of a functional group, and k is the number of proteins of the functional group in the predicted complex. As is well-known, the smaller difference between P-value and 0, the smaller possibility of protein complex possesses such function by chance and the larger possibility of protein complex encompasses special biological significance. Generally speaking, the main function of protein complexes corresponds to the minimum P-value. Therefore, we could predict the functions of unknown proteins by conferring the functions with the minimum P-value to identified complexes. The functional classification of proteins used in our paper was collected from the MIPS Functional Catalog (FunCat) database. FunCat [where . FunCat  is an anP-value less than 0.01. Under the same conditions, there is 1823 predicted protein complexes match with P-value less than 0.001. Meanwhile, only 128 identified protein complexes by using the clique percolation parameter k=3 match with P-value less than 0.01, and 121 complexes match with P-value less than 0.001 using the clique percolation parameter k=3. By the above function enrichment analysis, we could understand, the capacity of algorithm CP-DR identifying protein complexes with special biological significance is much higher than that of algorithm CPM.There are 1896 predicted protein complexes match with the known functional categories with P-value definition, the P-value is relational closely to the protein complex scale. Generally speaking, the larger size of protein complexes corresponds to relativity the smaller P-value. The size of protein complexes predicted by algorithm CP-DR is restricted by distance constraint, while the P-value of predicted protein complexes has not apparent change. For instance, the -log value of a protein complex predicted by algorithm CPM containing 18 proteins is 26. However, this complex is decomposed into three protein complexes by algorithm CP-DR and the -log value of each protein complexes corresponding to 23, 17, 14 respectively. There is full functional association information of two predicted complexes in Table P-value) of protein complex Table ER to Golgi Transport) completely consistency. The maximum -log of protein complex Fig.Mitosis M Phase), 14.07 (Protein Modification), 14.13.01.01 ), 16.01 (Protein Binding) and so on. All proteins except YGL240w correspond to 14.10 (Assembly of Protein Complexes) and 16.19.03 (ATP Binding) high consistency. As the fact that proteins in the same complex are of similar functions, we could predict new functions for known proteins. Thus, we can predict that the function known protein YGL240w is also involved in the functional categories 14.10 (Assembly of Protein Complexes) and 16.19.03 (ATP Binding) in real biological process, too. In fact, researchers have found the protein YGL240w absolutely has these functions. Furthermore, the functions annotated only for some individual proteins in the complex could also provide clues for studying protein function.According to the k-clique community of algorithm CPM, introduced distance restriction, proposed a new topological model for protein complexes and developed an algorithm CP-DR to identify protein complexes in large protein interaction networks based on the proposed topological model. Interaction networks are represented by undirected simple graphs and we generate predicted clusters in the networks by using clique percolation and distance restriction. The algorithm CP-DR could generate overlapping protein complexes, which is consistent with the fact that many of the known protein complexes are overlapping. Interesting questions for further research include how many functions a protein can have, how many processes a protein can participate in, and how heavily two protein complexes should overlap with each other.It is believed that identification of protein complexes is useful to explain certain biological progress and to predict functions of proteins. In this paper, we extended the definition of Sacchromyces cerevisiae. Many well-known complexes were detected in the protein interaction network. We tested the sensitivity, specificity and f-measure of our algorithm. The results have shown that our algorithm is suitability and efficiency in the protein interaction networks. We also predict the functions for un-characterized proteins and predicted new functions for the known proteins by minimizing the P-values of the predicted clusters. Our algorithm can thus be used to identify new protein complexes in protein interaction networks of various species and to provide references for biologists in their research on protein complexes.We applied the algorithm CP-DR to the protein interaction network of Saccharomyces cerevisiae is downloaded from MIPS (Munich Information Center for Protein Sequences) database. We remove all the self-connecting interactions and repeated interactions. The final network includes 4546 yeast proteins and 12319 interactions. We also collect from the MIPS database protein complexes annotated for Sacchromyces cerevisiae. We discarded those consisting of only one protein and the final remaining 216 manually annotated complexes are considered as the gold standard data. The largest complex contains 81 proteins, the smallest complex contains 2 proteins, and the average size of all the complexes is 6.31. We download the overlapping protein complexes identification tool CFinder from http://angel.elte.hu/clustering/. The proposed algorithm CP-DR has been implemented in C++.The protein interaction network of The authors declare that they have no competing interests.


Detailed information about protein interactions is critical for our understanding of the principles governing protein recognition mechanisms. The structures of many proteins have been experimentally determined in complex with different ligands bound either in the same or different binding regions. Thus, the structural interactome requires the development of tools to classify protein binding regions. A proper classification may provide a general view of the regions that a protein uses to bind others and also facilitate a detailed comparative analysis of the interacting information for specific protein binding regions at atomic level. Such classification might be of potential use for deciphering protein interaction networks, understanding protein function, rational engineering and design.Protein binding regions (PBRs) might be ideally described as well-defined separated regions that share no interacting residues one another. However, PBRs are often irregular, discontinuous and can share a wide range of interacting residues among them. The criteria to define an individual binding region can be often arbitrary and may differ from other binding regions within a protein family. Therefore, the rational behind protein interface classification should aim to fulfil the requirements of the analysis to be performed.We extract detailed interaction information of protein domains, peptides and interfacial solvent from the SCOWLP database and we classify the PBRs of each domain family. For this purpose, we define a similarity index based on the overlapping of interacting residues mapped in pair-wise structural alignments. We perform our classification with agglomerative hierarchical clustering using the complete-linkage method. Our classification is calculated at different similarity cut-offs to allow flexibility in the analysis of PBRs, feature especially interesting for those protein families with conflictive binding regions.The hierarchical classification of PBRs is implemented into the SCOWLP database and extends the SCOP classification with three additional family sub-levels: Binding Region, Interface and Contacting Domains. SCOWLP contains 9,334 binding regions distributed within 2,561 families. In 65% of the cases we observe families containing more than one binding region. Besides, 22% of the regions are forming complex with more than one different protein family..The current SCOWLP classification and its web application represent a framework for the study of protein interfaces and comparative analysis of protein family binding regions. This comparison can be performed at atomic level and allows the user to study interactome conservation and variability. The new SCOWLP classification may be of great utility for reconstruction of protein complexes, understanding protein networks and ligand design. SCOWLP will be updated with every SCOP release. The web application is available at  Protein interactions are essential for intra-cellular communication in biological processes. Proteins are composed of small units or domains that can physically interact together forming multi-domain protein complexes. A single protein can have several binding regions, and each region can engage distinct ligands, either simultaneously or at successive stages of signalling .In our previous work we developed the SCOWLP database , which cBinding regions in protein domains can form separated patches, but also some protein families bind through multiple binding regions with different ranges of residue overlapping. Furthermore, some observed protein interfaces are the result of non-biological artefacts  and are often difficult to distinguish from the biological ones, creating discrepancy among the current resources ,7. Some Hierarchical clustering comprises a whole family of clustering methods differing only on the manner inter-cluster distance is defined (the linkage function). The more common aggregation methods are single-, complete- and average-linkage. Complete and single-linkage are extreme procedures with completely different properties. Complete-linkage uses the similarity between the furthest pair of objects from two clusters. In contrast to these requirements, single-linkage only uses the nearest pair of objects from each cluster. Both methods have an extreme conception of homogeneity of a cluster. Single-linkage leads to grouping and may result in a few large and heterogeneous clusters . CompletWe present a classification of PBRs from all existing contacting domains from the SCOWLP database, which includes detailed information about proteins, peptides and solvent interaction. Peptide and solvent interactions are highly represented in the PDB and are highly informative in protein interactions . For ourThe PBRs classification extends the SCOWLP relational database by four additional tables describing the hierarchical classification at binding region, interface and contacting domain level. The content of the classification is given at similarity zero, which offers a general view of the regions that protein families use for recognition. In addition, our classification offers different similarity cut-offs to allow flexibility in the analysis of the PBRs. The classification of PBRs was performed as follows Fig. :wet spots) are also taken into account. We consider "interface" all domain-domain interactions; that means those belonging to the same protein and also to different proteins. SCOWLP contains 79,803 interfaces contained in 2,561 SCOP families. We grouped the domains participating in each interface by SCOP families, obtaining for each family a list of contacting domains with the residues forming part of the binding region.An accurate definition of the interacting residues is crucial to have a proper clustering of a family PBR. We extracted all protein interfaces from the SCOWLP database, in which the interactions are defined at atomic level and based on their physiochemical properties ,15. ProtA reliable alignment is indispensable to calculate the similarities among binding regions. For this purpose we used MAMMOTH, which has shown proven accuracy to structurally align protein families . We perfSi) based on the number of interacting residues that overlap and the length of both interacting regions by . This value is divided by the average number of the interacting residues in both domains excluding the interacting residues located in gap regions in the structural alignment (IRgaps).where Si, we clustered the binding regions of each SCOP family using the agglomerative hierarchical algorithm  visvs. variation of the interactions. In this example we easily are able to analyze (at structure and sequence level) all the interfaces of the PTB domain with different phospho-tyrosine peptides and their interaction patterns. In the example, the three main recognition regions described for the X11 PTB and a peptide motif from the Alzeimer's amyloid precursor protein  are displayed and structurally aligned with the recognition regions of other peptides known to bind PTBs in this region. Also, specific differences in the interaction pattern can be further analyzed individually by clicking on each PDB entry code. Analysis of the conservation/variability of the interactions describing an interface may be of great utility for understanding energetic and evolutionary aspects of protein interactions and for helping in rational engineering and design.In Figure Classification of the regions that a protein family uses to recognize binding partners is important for understanding the interactome. Protein binding regions are often irregular, discontinuous and can share interacting residues among them, making their clustering difficult, and arbitrary. A suitable classification requires proper measurements of similarity between protein binding regions and an appropriate clustering approach. Our approach consists on hierarchical clustering of the PBRs included in the SCOWLP database, which contains detailed interfacial information of proteins, peptides and solvent. We use the complete-linkage method and a similarity index obtained by mapping interacting residues in non-gap regions of pair-wise structural alignments. This approach provides a dilatation of the differences among clusters, making it suitable to isolate poorly separated clusters. In addition, we introduce flexibility in the usage of different similarity cut-offs for PBRs analysis. Our results show that, from 2,561 families containing binding regions, 65% use more than one binding region to interact. Furthermore, from all existing binding regions in SCOWLP, 22% are interacting with more than one protein family. In order to be able to analyze all family binding regions of the PDB in a detailed and comparative fashion we have implemented our PBR classification into the SCOWLP web application.The current SCOWLP classification and its web application represent a complete framework for the study of protein interfaces and comparative analysis of protein family binding regions. Mining of this information may be of great utility for understanding energetic and evolutionary aspects of protein interactions, reconstruction of protein complexes, understanding protein networks and rational ligand design..SCOWLP classification of PBRs is freely available at JT performed the classification of family binding regions. JT and MP designed the web application. JT and GA performed the PSAs. MTP coordinated and supervised the project. All authors have read and approved the final manuscript.


Mitochondria play a vital role in the energy production and apoptotic process of eukaryotic cells. Proteins in the mitochondria are encoded by nuclear and mitochondrial genes. Owing to a large increase in the number of identified mitochondrial protein sequences and completed mitochondrial genomes, it has become necessary to provide a web-based database of mitochondrial protein information.We present 'MitoInteractome', a consolidated web-based portal containing a wealth of information on predicted protein-protein interactions, physico-chemical properties, polymorphism, and diseases related to the mitochondrial proteome. MitoInteractome contains 6,549 protein sequences which were extracted from the following databases: SwissProt, MitoP, MitoProteome, HPRD and Gene Ontology database. The first general mitochondrial interactome has been constructed based on the concept of 'homologous interaction' using PSIMAP  and PEIMAP . Using the above mentioned methods, protein-protein interactions were predicted for 74 species. The mitochondrial protein interaction data of humans was used to construct a network for the aging process. Analysis of the 'aging network' gave us vital insights into the interactions among proteins that influence the aging process.http://mitointeractome.kobic.kr/.MitoInteractome is a comprehensive database that would (1) aid in increasing our understanding of the molecular functions and interaction networks of mitochondrial proteins, (2) help in identifying new target proteins for experimental research using predicted protein-protein interaction information, and (3) help in identifying biomarkers for diagnosis and new molecular targets for drug development related to mitochondria. MitoInteractome is available at  It plays a critical role in energy production, metabolism, apoptosis, cellular proliferation, and other essential cellular processes. Consequently, mitochondrial dysfunctions result in disorders ranging from energy metabolism defects to neuro-degenerative diseases such as Parkinson's disease and Alzheimer's disease . The assproteome , while KIn order to gain an in-depth understanding of the molecular functions and processes of mitochondria, it is essential to examine the interactions among its proteins. Here, we introduce 'MitoInteractome', a consolidated multi-species database for mitochondrial research that integrates information relevant to functions, pathways, diseases, non-synonymous SNP effects, protein-protein interactions, and physico-chemical properties. Further, we present an application of MitoInteractome in the construction and analysis of a protein interaction network for the 'aging' process.http://www.expasy.ch/sprot/ and including subsets from MitoP http://www.mitop.de:8080/mitop2/[http://www.mitoproteome.org/[http://www.hprd.org[http://www.geneontology.org[http://psort.ims.u-tokyo.ac.jp/[http://www.cbs.dtu.dk/services/TargetP/[http://urgi.versailles.inra.fr/predotar/predotar.html[http://mitointeractome.kobic.kr/supplement.php.The primary dataset for MitoInteractome was generated by performing a keyword search against SwissProt 0/mitop2/, MitoProeome.org/, HPRD ht.hprd.org, and Genyo.ac.jp/, 2) Targ/TargetP/, and 3) otar.html. Only thConstruction of complete MitoInteractome database is illustrated in Figure http://psimap.com, http://psibase.kobic.re.kr) [Protein-protein interactions are diverse, but all occur in a highly specific manner determined by the structural and physico-chemical properties of the interacting partners. We used two methods to predict protein interactions: PSIMAP  (c.re.kr) ,13 and Pc.re.kr) .http://www.rcsb.org/pdb domains based on the Euclidean distance between them. Two domains are assigned as 'interacting' if they have five or more atoms within 5 Ǻ distance. To construct the mitochondrial proteomes, we first assigned known 3D structural families in SCOP [-4. However, the same region of a protein sequence could be assigned and overlapped by more than two SCOP families, each of which falls within the same significance level. In these cases, we accepted overlaps of up to 15 amino acid residues in the alignments and regarded them as two separate domains for a given protein sequence. Using PSIMAP, we found 36,359 structural interaction pairs from the mitochondrial proteomes.PSIMAP is a structure based interaction prediction method. It predicts interactions between known PDB  in SCOP  to mitocStructural interaction information tends to provide more number of predictions as many specifically different sequences share the same structure. Therefore, experiment based sequence based PPI data are usually more specific. Our usual step is to predict many partners using structural interaction information and then apply sequence based experimental (more specific incident of protein-protein interaction) PPI data to filter out the first.http://bond.unleashedinformatics.com[http://dip.doe-mbi.ucla.edu[http://mint.bio.uniroma2.it/mint[http://www.ebi.ac.uk/intact[PEIMAP  uses experimental data available in BIND atics.com, DIP htt.ucla.edu, MINT ht2.it/mint, IntAct uk/intact, and HPRhttp://www.ncbi.nlm.nih.gov/Omim/[http://mitointeractome.kobic.kr/search.php.Mitochondrial dysfunction is associated with a large number of aging-related neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, Amyotrophic lateral sclerosis, and Huntington's disease . To dategov/Omim/. The numgov/Omim/. Informahttp://www.ncbi.nlm.nih.gov/projects/SNP/ build 129 [http://genetics.bwh.harvard.edu/pph/[It is widely accepted that polymorphisms in the human genome are expected to play a key role in defining the etiologic basis of phenotypic differences amongst individuals in the aspect of drug responses and common disease predisposition . Non-synuild 129 , associa.edu/pph/, a tool http://www.genome.jp/kegg/[http://www.brenda-enzymes.info/[Pathway information is vital for successful quantitative modeling of biological systems. Mitochondria have been implicated in the origin of diseases, because of their crucial role in energy production, in nuclear cytoplasmic signal integration, and in the control of metabolic pathways. Mitochondrial pathway information comprising of pathways, enzymes involved, and their respective KEGG Orthology was collected from KEGG .jp/kegg/. The patmes.info/.MitoInteractome is accessible through a web interface that is powered by PHP and JavaScript. The data is stored in retrievable MySQL tables.As shown in Figure http://mitointeractome.kobic.kr/downloads.php. The help page explains the function of every search interface.We developed a Java based network viewer to graphically visualize protein-protein interactions Figure . InformaThe process of 'aging' is one of the active fields of biological research, the study of the biological process aspect of which is popular as 'Biogerentology'. It is common knowledge that the onset of several neurodegenerative disorders such as Alzheimer's disease, Parkinson's disease, Leigh's syndrome etc. are of progressive nature with aging. Several studies in the past have indicated association of the aging process with mitochondria -31. SincA list of 42 distinct proteins associated with the 'aging' process was obtained from the GO database . These pe.g Gene name: GRB2, ABL1, TP53, ANDE1) were observed to be involved in at least one aging related disease, while proteins  that had low strengths were observed to be mostly house-keeping gene products.A centrality measure - "Strength of a node" - was introduced, which is defined as the sum of the weights of other nodes it is interacting with. Strength for a weighted network is analogous to 'Connectivity' for an un-weighted network. Strength of a node in the present network would capture the size of its neighborhood, constituted by the interacting proteins, as well as the uniqueness of the neighborhood, derived from the domain conservation analysis. Proteins possessing higher strengths  plays an important role in aging. Matheu et. al. [TP53 gene acts as a tumor suppressor and also induces apoptosis depending on the physiological circumstances.It has previously been reported that the p53 protein : Bioinformatics, available online at http://www.biomedcentral.com/1471-2105/10?issue=S15.Other papers from the meeting have been published as part of


A new network decomposition method is proposed that uses both a global metric and a local metric to identify protein interaction modules in the protein interaction network.  We propose a new network decomposition method to systematically identify protein interaction modules in the protein interaction network. Our method incorporates both a global metric and a local metric for balance and consistency. We have compared the performance of our method with several earlier approaches on both simulated and real datasets using different criteria, and show that our method is more robust to network alterations and more effective at discovering functional protein modules. Protein complexes are building blocks of cellular components and pathways. A comprehensive understanding of a biological system requires knowledge about how protein complexes are assembled, regulated, and organized to form cellular components and perform cellular functions. The emergence of a variety of genomic and proteomic techniques to systematically obtain such information has generated an enormous amount of data -11. Howeet al. ,  and . There are more BCD complexes in terms of absolute number with P value less than 1e-15 on both the unfiltered and filtered datasets.In addition to the MIPS protein complex dataset we also evaluated the biological significance of predicted protein modules by quantifying GO term co-occurrences using the SGD GO Term Finder . The GO P values. Figure P = 1.9e-12) of a spindle-assembly checkpoint complex that is crucial in the checkpoint mechanism required to prevent cell cycle progression into anaphase in the presence of spindle damage [P = 9.8e-17) including members from the Set3 histone deacetylase complex  [The number of predicted protein complexes is larger than the number of known protein complexes compiled in the MIPS complex dataset, and many predicted protein complexes do not overlap with MIPS complexes. Among these unmatched predicted protein complexes, some are likely to be true functional protein modules because the GO terms in these complexes are greatly enriched as indicated by low e damage   , protein1, Sif2) , protein1, Sif2) ,33 and tP < 1e-15) or they overlap with MIPS complexes (overlap = 100%). Generally, the protein modules falling within the above two categories can be viewed as functional modules. The BCD algorithm outperforms the other three algorithms in terms of identifying more functional protein modules on the unfiltered dataset. The MCL algorithm predicts more functional protein modules than our BCD algorithm does on the filtered dataset. In addition, all four algorithms predict a substantial number of complexes that do not overlap with MIPS or in which GO term co-occurrences are insignificant. However, these are potentially novel functional complexes for biologists to explore further.Table In all experiments, the results on the filtered data are consistently better than the results on the original data. For example, in Table Protein interaction networks are examples of complex systems that are difficult to understand from raw experimental data alone. Methods to organize, filter, extract significant features and display these data are critical to understanding these systems. A number of network partition algorithms have been proposed to find modular structures in protein interaction networks ,34-39. Oth annotated complex and column j to the jth cluster and the value of a cell Tij indicates the number of proteins found in common between complex i and cluster j. The contingency table has n rows (complexes) and m columns (clusters).We computed the geometric accuracy and separation by following the approach described in the study by Brohee and van Helden . BrieflyFirst, we define complex-wise sensitivity i is the number of proteins belonging to complex i. To characterize the general sensitivity of a clustering result, we compute a clustering-wise sensitivity as the weighted average of where NSecond, we calculate a cluster-wise positive predictive value Tj is the marginal sum of a column j by:where To characterize the general PPV  of a clustering result as a whole, we compute a clustering-wise PPV as the weighted average of The geometric accuracy (Acc) indicates the tradeoff between sensitivity and predictive value. It is obtained by computing the geometric mean of the Sn and the PPV by:From the contingency table, we derive relative frequencies with respect to the marginal sums, either per row:or per column:We then define the separation as the product of column-wise and row-wise frequencies by:The complex-wise separation and the cluster-wise separation Sepco and clustering-wise Sepcl values are computed as the average of To estimate a clustering result as a whole, complex-wise Sepco and Sepcl by:We then compute the geometric separation (Sep) as the geometric mean of BCD, Betweenness-Commonality Decomposition; ECC, edge clustering coefficient; GN, Girvan and Newman; GO, Gene Ontology; MIPS, Munich Information Center for Protein Sequences.CW, CD and SH were involved in the study design. CW developed the method, carried out the study, and wrote the manuscript. QY was involved in the evaluation of the method. All authors revised the manuscript, read, and approved the final manuscript.The following additional data are available with the online version of this paper. Additional data file Hierarchical decomposition trees of a yeast transcriptional sub-network by different algorithms.Click here for filePredicted protein interaction modules by the BCD algorithm on the unfiltered dataset.Click here for filePredicted protein interaction modules by the BCD algorithm on the filtered dataset.Click here for file


Determining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problem's complexity and scale. Identifying a protein's function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity.We find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder, but co-occurrence data still proves beneficial.Co-occurrence data is a valuable supplemental source for graph-theoretic function prediction algorithms. A rapidly growing literature corpus ensures that co-occurrence data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. Though arguably biased toward known genes, co-occurrence data provides critical additional links to well-studied regions in the interaction network that graph-theoretic function prediction algorithms can exploit. The putative characterization for unknown proteins has traditionally relied on sequence homology, for example as assessed by BLAST score. This approach is inadequate for proteomic-wide function identification as it has a failure rate of 20–40% in newly sequenced genomes . SourcesNew methods for proteomic-scale function prediction which do not rely on sequence homology draw from high-throughput data to make inferences, including several techniques that use protein-protein interaction graphs ,3-8. ProPrevious research has significantly improved algorithmic performance by integrating more diverse interaction types, such as gene expression data, genetic interactions, or phylogenetic based features ,9,10. InThis paper examines closely the use of Medline literature abstracts as a potential high-throughput annotation source. Literature offers a valuable resource as the original and historical source of information contributed by biologists; however, much of that information has yet to be human-curated in a computable form. Moreover, the current pace of paper submissions outstrips curators' ability to extract information. Thus, a popular trend is to develop sophisticated natural language processing methods to mine free-text data for protein relationships. Previously, various researchers have tried directly mining text for functional information -17, yet In this paper, we consider the straight-forward approach of mining for potential interactions by identifying co-occurrence of protein names in Medline abstracts, similar to the use of co-occurrence data by Schlitt  as a wayWhile we could further combine co-occurrence data with other interaction information, here we purposefully isolate the literature data source and quantify its individual contribution as a supplement to protein interaction data. The originality and benefit of our study is many-fold. Firstly, to our knowledge, there has been no systematic study of the use of literature-based information in graph-theoretic function prediction algorithms. While systems such as STRING , PubGeneSecondly, nearly all previous demonstrations of function prediction have been benchmarked on a single organism, typically yeast. Even when algorithms use multi-organism data to make predictions, particularly for prokaryotes e.g. ,24), the, the24])et al. /[nxny]) and Rmin and Rmax are the lowest and highest R values over all pairs x and y. The hypergeometric score [l aswhere ic score  calculatp is the hypergeometric distribution:where This value computes the significance of co-occurrence, such that smaller numbers mean the observed co-occurrence is less likely by chance, i.e. low numbers mean high confidence. To scale the values to be analogous to the other measures, where high values mean high confidence edges, we define the hypergeometric-based co-occurrence measure asx and y becomes significant when the individual protein occurrence frequency of x is also low since it implies that x, though hardly mentioned, is always mentioned in conjunction with y. This is true regardless of the occurrence frequency of a protein y.One potential disadvantage of these measures for our application is the fact that each is symmetric with respect to the contribution of the protein pair and does not emphasize the frequency of co-occurrence relative to the individual occurrence frequencies. For example, a low co-occurrence frequency of two proteins Asymmetric Co-occurrence Fraction (ACF) which includes a bias towards genes mentioned infrequently:An asymmetric measure is important for biology applications since some genes are more well studied than others, and there is particular interest in genes with little prior information. For this reason, we define the ACF = ACF yet we label the measure as asymmetric to emphasize the bias towards less characterized proteins. We compare our new measure to the two alternatives, MUT and HY G, to investigate whether important relationships are overlooked under symmetry. To reduce false positives for any of the three co-occurrence based measures, we add the additional requirement that nxy > 1, otherwise the co-occurrence value is set to zero.Mathematically speaking, the measure itself is symmetric, since clec resolved to 83 distinct Entrez identifiers of C Type lectin genes in worm, resulting in all pairwise interactions among those genes, all with weights of 1.0 regardless of the co-occurrence measure. The consequence is that at high co-occurrence thresholds, the graphs contain many clusters representing gene families [see Additional files Regardless of the co-occurrence measure used, the method for handling ambiguity in normalizing gene names artifactually inflates the values within gene families. For example, the text string ACF ≥ 0.3.Protein-protein interactions were extracted from the Database of Interacting Proteins (DIP)  for yeasmap2slim.perl script also available from the GO website. According to Ofran et al. [Yeast proteins were annotated using the MIPS  functionet al. [rUetz = 0.37. Interactions supported by more than one source received a combined weight based on the scores of the individual contributors, using the noisy-or function [i(1 - ri) where the product was taken over all experiment groups i where the interaction was found and ri was the corresponding reliability of group i. Thus, if the Gavin experimental group had a protein-protein interaction that was also in Uetz, the MIPS Gavin evidence score of rGavin = 0.67 was combined with the rUetz = 0.37 for Uetz using noisy-or to increase the final score for the edge strength. When using MIPS, an annotation was shared if for the depth of annotation examined, both proteins had identical annotation, so YOR396W, in functional category 10.01.03.01, and YPL001W in functional category 10.01.09.05, would share an annotation at depth 2 (both are 10.01 or "DNA Processing") but would not share an annotation at depth 3. For the co-occurrence datasets created from a particular threshold, all edges which meet or exceed the threshold were considered as a single group when calculating the reliability, ignoring the actual value assigned to the edge by the co-occurrence measure.Following the approach of Nabieva et al.  and the et al. , the funfunction . The noiet al. (2005), Majority and Functional Flow, were examined. The Majority algorithm  predictPerformance of prediction algorithms using a given graph was measured using a modified receiver operator characteristic (ROC) curve that measures the number of true positive predictions against the number of false positive predictions as the prediction strength threshold changed. As in the previous work , when aUsing the most general definition of co-occurrence, whereby an interaction exists between two proteins mentioned at least twice together in the literature (denoted COLIT), co-occurrence data was a significant source of interactions for all organisms Table . The cone.g., mechanosensory abnormality (mec genes) or synthetic lethal with mec-8 (sym genes), causing co-occurrence to implicitly discover genetic relationships. The interactions offered by COLIT or GENETIC data were a significant addition to the PPI data  typically assigned a mid-range confidence level to the majority of edges  tended to assign a high value to nearly all edges  network Figure . The strPerformance improvements of Functional Flow were not due merely to the presence of additional interactions, but rather to the type of relationship captured by the data sources. Supplementing the PPI network with a similarly-sized set of genetic interactions did not show as marked a performance gain as incorporating co-occurrence data Figure  and 3. ICo-occurrence data also boosted the performance for the local algorithm, Majority which uses the majority assignment of immediate neighbors method Figure . IncludiHYG and certain thresholds in yeast using Molecular Function. The worst often occurred for the 0.0 threshold, the simplest definition of co-occurrence, demonstrating the benefit of moving beyond a simple binary indicator of co-occurrence to methods based on confidence levels.The performance boost offered by including literature data was seen regardless of the method for determining co-occurrence. The set of interactions offered in the co-occurrence data varied depending on a threshold. The best and worst combinations of co-occurrence measure and threshold  have neighbors in the graph based solely on PPI data (3% have PPI and COLIT edges) versus 42% of the known proteins (45% have PPI and COLIT). Worm has a similar ratio with 80% PPI ONLY edges for the uncharacterized versus 53% PPI ONLY edges for the knowns, while fly follows with 93% PPI ONLY edges for the uncharacterized versus 60% PPI ONLY edges for the knowns.The real concern then is how co-occurrence data can affect the prediction algorithm performance since the majority of true unknowns are solely connected to the rest of the graph through high-throughput protein-protein interactions. This question is particularly relevant in worm and fly with a larger percentage of truly uncharacterized proteins. The performance gain of the global Functional Flow algorithm over the local Majority algorithm precisely demonstrates how information can flow from a region of more well-characterized genes, where co-occurrence data is present, through regions surrounding the true unknowns and thus dominated by physical interaction data. In fact, the average minimum path distance between unknown proteins in yeast and proteins appearing in a least two Medline abstracts (thus likely to have a co-occurrence link) is 1.41 in yeast, 1.60 in worm and 1.387 in fly. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit.ACF or MUT co-occurrence edges differed in yeast with respect to worm or fly  > 0.9. Note that at this threshold, large clusters of like color indicate protein families.Yeast co-occurrence graph where ACF is greater than .9. The complete PPI and co-occurrence network using ACF scoring at the highest threshold. Nodes correspond to yeast proteins and are colored by GO SLIM categories such that white nodes indicate Unknown Function. Edges between nodes Click here for filex and y indicate ACF > 0.9. Note that at this threshold, large clusters of like color indicate protein families.Worm co-occurrence graph where ACF is greater than .9. The complete PPI and co-occurrence network using ACF scoring at the highest threshold. Nodes correspond to yeast proteins and are colored by GO SLIM categories such that white nodes indicate Unknown Function. Edges between nodes Click here for filex and y indicate ACF > 0.9. Note that at this threshold, large clusters of like color indicate protein families.Fly co-occurrence graph where ACF is greater than .9. The complete PPI and co-occurrence network using ACF scoring at the highest threshold. Nodes correspond to yeast proteins and are colored by GO SLIM categories such that white nodes indicate Unknown Function. Edges between nodes Click here for file


Nowadays modern biology aims at unravelling the strands of complex biological structures such as the protein-protein interaction (PPI) networks. A key concept in the organization of PPI networks is the existence of dense subnetworks  in them. In recent approaches clustering algorithms were applied at these networks and the resulting subnetworks were evaluated by estimating the coverage of well-established protein complexes they contained. However, most of these algorithms elaborate on an unweighted graph structure which in turn fails to elevate those interactions that would contribute to the construction of biologically more valid and coherent functional modules.In the current study, we present a method that corroborates the integration of protein interaction and microarray data via the discovery of biologically valid functional modules. Initially the gene expression information is overlaid as weights onto the PPI network and the enriched PPI graph allows us to exploit its topological aspects, while simultaneously highlights enhanced functional association in specific pairs of proteins. Then we present an algorithm that unveils the functional modules of the weighted graph by expanding a kernel protein set, which originates from a given 'seed' protein used as starting-point.The integrated data and the concept of our approach provide reliable functional modules. We give proofs based on yeast data that our method manages to give accurate results in terms both of structural coherency, as well as functional consistency. In the post genomic era one of the most challenging tasks is to reveal modular structures in biological networks, in order to comprehend the function and the dynamics of a living cell ,2. The vThe functional module is defined  as a groThe determination of small-scale functional modules, in the form of protein complexes  includedIn this direction recent studies perform clustering after transforming the graph to its weighted correspondent. Various methods have been applied for weighting a graph. Pereira et al.  weightedThe development of high throughput techniques, such as yeast two-hybrid system , proteinOn the other side protein-protein interaction data reflects the collaboration of proteins to achieve a common goal. Several initial studies ,17 attemIn our study we introduce a new method for integrating gene expression data and protein-protein interactions (PPI) in order to determine functional modules. Specifically, given a graph describing a set of proteins and the experimentally determined interactions among them, we assign a non-negative weight to each one of these interactions. This weight descends from clustering the gene expression profiles of the corresponding proteins, an approach that to the best of our knowledge has not been used before in the literature. Our goal is to discover biologically relevant PPI subnetworks, out of a larger network, whose proteins interact significantly. Therefore, after the creation of the weighted graph we suggest a new algorithm for the determination of functional modules within the original PPI graph starting from a kernel protein group that originates from a 'seed' protein.Saccharomyces cerevisiae that the integrated approach we present manages to adequately and solely identify coherent modules and at the same time outperforms other methods in the literature.We prove based on data of Saccharomyces cerevisiae, in order to detect valid functional modules, in terms of protein complexes they contain. We created a weighted PPI graph, in which the weight of the interactions originates from clustering the gene expression profiles of the corresponding proteins. Specifically the weight of a PPI derives from a metric that takes into account the distance of the corresponding gene expression profiles from the centroids of their clusters, as well as the distance between the two cluster centroids themselves. Next we applied at the enriched PPI graph a new algorithm called DMSP (Detect Module from Seed Protein), whose role was to construct functional modules starting from a 'seed' protein that belongs to our dataset. All the steps of this procedure are described in detail at the Methods section.In our approach, we have reinforced the simple graph structure by integrating gene expression profiles and protein interactions of The derived modules are evaluated in terms of functional enrichment in GO terms, structural coherency and coverage in protein complexes. Additionally we compare the modules of our integrated method with the modules descending from PPI data only (named hereafter PPI method), after the application of the algorithm described in the study of Wu and Hu   primase-NOT-CCR4 complexes, identified with CDC39 as seed protein Table . The DNAFinally, we present the module of ADA-SAGA-TFIID complexes Figure  that wasThe defect of the algorithm used in the cross-checked study, is that it tends to build large modules in order to capture certain complexes, whereas DMSP builds small-sized modules where almost all members are part of a complex. In this way the condition proposed by Spirin and Mirny , that thNevertheless, if we want to examine thoroughly our approach, we have to extend the conducted experiments beyond isolated and individual examples. In a task like this the eloquent question is how we choose the 'seed' proteins. The answer lies in the concept of protein complexes that have the leading role in the overall evaluation of our method. Specifically we chose to examine complexes with more than 5 members and 80% coverage in terms of proteins included in our data set. The first limitation is based on the fact that it is very probable for small protein complexes to be contained in large functional modules by chance. The second limitation deals with the way we gathered data for this study . So there are cases where the method fails to give good results for certain complexes. In other words while the information we have included in the study is adequate, for certain cases can be insufficient. Most of the studies, that exploit PPI data, avoid weeding out the false positive and false negative interactions, thus this 'burden' can result in misleading functional modules. However, the limitations of our dataset offer the advantage of predicting highly confident functional modules leaving though some space for interactions that are not yet established but have high probability to be correct. One example displaying this characteristic is the module of H+-transporting ATPase vacuolar that was built from VMA8 as seed protein. The role of V-ATPase complex in eukaryotic cells (15 members) is to couple the energy of ATP hydrolysis into proton transport across intracellular and plasma membranes . Our PPIWe return now to the issue of deciding a set of proteins that will be used as 'seeds' for the construction of functional modules throughout the whole data set, in order to evaluate better our method. Towards this goal we have followed a methodology where we sorted the members of a certain complex, that fulfils the two limitation discussed above (i.e. more than 5 members and more than 80% in data coverage). Specifically we sorted its members starting with the one that had the largest number of neighbours (independently of the module) down to the member with the smallest number of neighbours. Subsequently we created a set with the top 20% of the sorted members. These protein sets were used as 'seed' proteins in our method. Our results show that a large amount of members in the initially selected protein set can be used as 'seed' resulting always in the same module. An example is the module of 20S proteasome complex Figure  which isIn order to test the validity of the functional modules determined by our integrated approach we will employ three criteria. The first one includes the Gene Ontology annotation scheme to gain insights in the common underlying biological processes of the modules. The second one utilizes the metric of connectivity density to show how well connected the determined modules are, while the last one uses a metric we call complex coverage for checking their biological relevance. At this point it is worth mentioning that in order to show that our integrated approach is superior to a method restricted to PPI data only, we applied the W&H to our PPI dataset and examined the resulted modules with all 3 validation criteria mentioned above. However the forthcoming comparisons have different interpretation from the case study evaluation where we cross-checked our modules with the modules of the associated study as mentioned in the literature.diss = 1 - aij = 1-|cor |6, as a dissimilarity measure where xi represents the expression profile of the i-th gene.Additionally and in order to have a more coherent evaluation we compared the results of our approach in terms of functional enrichment analysis with a method using gene expression data only. Specifically following the methodology described in ,37 we haReturning to the issue concerning the validation check of the DMSP extracted functional modules, we firstly substantiated the biological significance of our modules by estimating the GO biological process term co-occurrence with the use of the SGD GO Term Finder . This toThe verification of how well connected are the members of a module will be resolved by employing the connectivity density. Connectivity density is the ratio of the total in-module degrees of the vertices to the total number of their connections. This metric has been used in many other similar studies . The conBased on the concept of connectivity density we tested the capabilities of our algorithm by tuning the parameters from the zenith (strictest) up to their nadir (loosest) and we checked how connectivity density varies accordingly. As we see in Figure Another worth mentioning aspect of our integrated method is the adaptability of our algorithm concerning the size of the module versus the size of the kernel protein set. In Figure -10. P-value is a measure concerning the degree up to which a module manages to capture a particular complex. This metric is mathematically described by:The extracted functional modules were evaluated from the biological point of view by a metric that takes into consideration the number of protein complexes that have been successfully encapsulated in a module. Before describing it, we are inserting a new term named basic complex. Basic complex is a complex within a module where the p-value is less than a certain limit which in this work is set to be 10i is the members of the i-th functional module, NCt is the size of a real basic complex, and NFt the size of the corresponding determined complex. L determines the number of basic complexes within the module. Value of R(Mi) ranges from 0 to 1, with 1 being the ideal case where our method manages to find all the proteins of the all basic complexes and 0 as the extreme case where no member of any basic complex has been found . In Figure The post genomic era poses two closely connected challenges. The first challenge is the consolidation of all kinds of high-throughput data, which in fact all describe the same complex dynamics of the living cell but through different perspectives. The second challenge is to unlock the hidden biologically meaningful structures  that lie in these integrated constructions. Our method is based on the integration of protein interaction and gene expression profile data, whose association has already been validated by other studies. We overlaid the information descending from gene expression clustering as weights onto a PPI yeast graph, with quite highly confident interactions. This accomplishment fulfils the need for consolidation.Subsequently, we propose a new algorithm (DMSP), which constitutes subnetworks that originate from a kernel protein set built up from a 'seed' protein. In order for us to characterize these subnetworks as functional modules we checked their biological relevance. This was achieved through some criteria such as the functional enrichment of the resulting modules in GO terms, their connectivity density, their coverage in known complexes and the degree of resemblance between the modules of our integrated approach and the modules of an associated work. Our results show that the majority of our modules have both connectivity density as well as R, over 0.7. Additionally the modules of our integrated method outperform the corresponding modules of the PPI method in all validation criteria. Thus, we succeeded to fulfil the second expectation mentioned above.It is obvious that the concept of our method can be extended to any set of cellular and genetic data. In future work we plan to use this integrated approach and the concept of DMSP as a basic component for an algorithm that will perform graph clustering in the overall PPI network, again having as goal the identification of functional modules as well as the functional dependencies between modules.The modeling of PPI networks by simple graph structures is common in many applications, including the determination of protein complexes, within protein networks. Protein complexes seem to have a corresponding analog in the graph structure-like of the protein network. This analog corresponds to dense subgraphs of the initial graph, in other words proteins of a specific complex are found to be highly interactive with each other . FollowiA common problem that appears when dealing with protein interaction data, obtained by high-throughput techniques ,43,44 isWe used yeast expression dataset to evaluate our method. The data contains cell cycle related profiles using cdc15 synchronization over three cell cycles . The exp-3 for accepting a functional category to characterize a cluster) were found in the case of 16 clusters.The initial set of 2985 genes was clustered in 16 groups according to the fuzzy c-means algorithm. It is worth mentioning that the number of clusters was appointed by a cluster validation criterion, which determined the range of clusters number. There are many clusters validation criteria in the literature that could be used for our purpose. We have used the well studied case of the Xie-Beni validity index . This crA first observation concerning the range of the resulting cluster population is that the minimum number of genes in a cluster is 82 (i.e. in cluster 16) and the maximum is 376 (i.e. in cluster 1). In Table In our approach we chose to unify the above types of data for various reasons. Firstly PPI data from high-throughput techniques is currently flooded with false interactions . Also prOn the other side gene expression data provides information of the genome under many different experimental conditions despite the large amount of inherent noise. In literature there are studies like  that exaIn order to keep the advantages from both sides we focused on a highly confident PPI network and used the gene expression data as reinforcement. In other words the PPI network is the protagonist, whereas the gene expression data reassures the entrance of certain interactions into the modules, even if they are not favoured by the topology. In this way our goal, i.e. to identify functional modules on the PPI network, is accomplished with great success, since we manage to lessen up to a great degree the drawbacks of each kind of data.x, Ky of these clusters. Then, we calculate the distance of each gene from its centroid and the distance between the two centroids.The protein-protein interaction network of yeast is represented as a graph G. The vertices of the graph are the set of unique proteins and therefore in our case is |V| = 2985, whereas the edges of the graph represent the interactions |E| = 8081. Below we explain our methodology of adding weights to the edges by exploiting the information of the gene expression profiles mentioned above. In order to add weight to an interaction between two proteins x and y, we find the clusters C(x) and C(y) where they belong and the corresponding centroids KThe weight of the PPI interaction is given by the metric described as:1 and n2 add an extra confidence score to the factors of the weight function. They can have the same or different values according to which member (if any) of the function we want to enforce. We have selected n2 > n1 because we consider the distance between centroids more significant comparing to the distance of each gene from its centroid. This selection was driven by the fact that there is noise (outliers) in the gene expression profiles.||·|| stands for the distance metric and there are many ways of measuring it (e.g. Euclidean). The constants nFollowing the principle that similar expression profiles are associated with similar function , the valOn the other hand there are many cases where proteins of the same complex may have quite different (e.g. transient complexes) or even inverse expression profiles. As a result an interaction in such a pair of proteins would fail to be emphasized by the above metric and thus not preferred in comparison to another smaller weighted interaction. Nevertheless, the proposed algorithm that we will describe in detail in the following section, manages to overcome problems like these by incorporating information given by the rest weighted neighbours of such an interaction, during the construction of a functional module.In this section we will give, after some preliminary concepts, a description of the proposed algorithm named Detect Module from Seed Protein (DMSP). DMSP builds functional modules by expanding the kernel neighbourhood generated by a 'seed' protein. In the implementation presented here, both the 'seed' protein as well as the extracted functional module, are part of a larger network which can be represented by a weighted graph structure.1 of a larger graph G we define the internal degree |NG1INT| as the number of edges connecting x with other vertices belonging to G1 and external degree as the number of nodes with which x is connected and exist in G but do not belong to G1.As we have already mentioned, in the approach we have followed, we combine gene expression profiles and PPI data, in the form of a weighted graph, G. By N(x) we denote the neighbours of a node x, or in other words the set of nodes that are connected to x. Then, the degree of x is equivalent to the number of neighbours of x |N(x)|. For a given subgraph G1 over |NG1INT|:The above concepts can be expanded to the weighted graphs easily. Weighted degree of a node is the sum of weights of the edges between x and its neighbours divided by |N(x)|. Weighted internal degree of a node x is the sum of weights of the edges between x and its neighbours within GCorrespondingly we define the term of weighted external degree.w(G), is the sum of the weights of actual edges over the edges among all nodes in G:The density of a graph G is generally measured by the proportion of the number of edges in the graph to the number of all possible edges, which is equal to |V|(|V|-1) for an undirected graph. Weighted density of a graph or subgraph DThe algorithm proposed in this paper operates in two phases. Firstly accepts one 'seed' protein and selects a subset of its most promising neighbours, subsequently expands this initial kernel to accept more proteins. This expansion is based on certain assumptions, concerning the number of neighbours for the specific protein as well as the weights of these connections.In the first stage of the algorithm only a certain number of the neighbours of the 'seed' protein (named hereafter s) is selected. These adjacent nodes are sorted in descending degree of significance and this subset of nodes – proteins is named kernel.The two criteria by which the original kernel is selected are the density of the kernel and the weighted internal and external degrees of it.Initially, the kernel is equal to all the neighbours of s:i belonging to Kernel(s) we find the NINT(Ks), NEXT(Ks), as well as the βINT and βEXT. The objective for selecting the kernel of the seed node is two-fold. Firstly we check so that the number of edges of a kernel node within the rest of the kernel is larger or at least equal to the number of the edges that a node has outside the group. We accomplish this by requesting for the internal and external degrees of each node:Then for each one of the neighbours u1 to have value over 45%. At the same time and after we have confirmed that a selected node fulfils the first condition, we request that the same node has smaller weighted internal degree than its corresponding weighted external degree:In this study we selected pNodes that fail to pass the above criteria are discarded, while those that do, are sorted based on the level that each one of them manages to do so.w(Ks) as:This original subset of proteins can be furthered distilled, in order to acquire an even more coherent kernel. This can be achieved by minimizing DIn this step, DMSP removes one at a time, each one of the sorted per significance nodes starting from the most insignificant until it reaches a minimum value of weighted density.The second stage of the algorithm receives as input the selected kernel of the 'seed' protein and iteratively adds adjacent nodes based again on certain criteria. The first criterion the algorithm checks is the same as the first one of the initial stage of the algorithm. After this criterion has been checked then we select a node to be added to the neighbourhood, if it satisfies the following:s), we select the constant p2 to be anywhere between 0.9 and 1.0. Relation (8) states that in order for an adjacent node ui of some kernel node v, to become member of the module, its weight must be less or equal to a specific percentage of the weighted internal degree of node v. Below we describe the pseudocode for implementing the function of the second stage of DMSP, named Determine_Module, which is responsible for the final determination of the functional module:G is the final module that is built from the initial kernel N(v) ≡ N(v) ∩ Ks      b. For every ui in N(v)      c. GINT, NGEXT         i. Calculate NIf v ∈ Ks         ii. i) > p1            1. R1: IO > p2            1. R1: IO is true then R2 = true else R2 = false         v. if R1 = true AND R2 = true         vi. G ≡ G ∪ ν            1. s, p1, p2, G)            2. G = Determine_Module(KEnd         vii. End      d. End   III. As we can depict from lines (ii.1), (iii.1) of the pseudocode there are two percentage values describing the relation of internal and external neighbours as it is calculated in equation 5. The distinction of this value depends on whether the current node is a direct neighbour of the kernel or not. In this way we have a two-layer scheme where we retain a looser criterion for immediate neighbours and a stricter one for the remote neighbours of the initial kernel.IAM conceived the integration method, the algorithm, and prepared the data sets. KD was responsible for writing the main body of the text and especially for the biological aspects of the study. All the above actions were supervised by AB. All authors read and approved the final manuscript.


The Dictionary of Interacting Proteins (DIP)  is a large repositoryof protein interactions: its March 2000 release included 2379 protein pairs whoseinteractions have been detected by experimental methods. Even if many of thesecorrespond to poorly characterized proteins, the result of massive yeast two-hybridscreenings, as many as 851 correspond to interactions detected using direct biochemicalmethods.        We used information retrieval technology to search automatically for sentences inMedline abstracts that support these 851 DIP interactions. Surprisingly, we foundcorrespondence between DIP protein pairs and Medline sentences describing theirinteractions in only 30% of the cases. This low coverage has interesting consequencesregarding the quality of annotations (references) introduced in the database and thelimitations of the application of information extraction (IE) technology to MolecularBiology. It is clear that the limitation of analyzing abstracts rather than full papers and thelack of standard protein names are difficulties of considerably more importance than thelimitations of the IE methodology employed. A positive finding is the capacity of the IEsystem to identify new relations between proteins, even in a set of proteins previouslycharacterized by human experts. These identifications are made with a considerable degreeof precision.        This is, to our knowledge, the first large scale assessment of IE capacity to detectpreviously known interactions: we thus propose the use of the DIP data set as a biologicalreference to benchmark IE systems.