Most cellular processes are carried out by multi-protein complexes, groups of proteins that bind together to perform a specific task. Some proteins form stable complexes, while other proteins form transient associations and are part of several complexes at different stages of a cellular process. A better understanding of this higher-order organization of proteins into overlapping complexes is an important step towards unveiling functional and evolutionary mechanisms behind biological networks.functional groups) within a protein interaction network. We develop a graph-theoretical framework that enables automatic construction of such representation. We illustrate the effectiveness of our method by applying it to TNFα/NF-κB and pheromone signaling pathways.We propose a new method for identifying and representing overlapping protein complexes (or larger units called The proposed representation helps in understanding the transitions between functional groups and allows for tracking a protein's path through a cascade of functional groups. Therefore, depending on the nature of the network, our representation is capable of elucidating temporal relations between functional groups. Our results show that the proposed method opens a new avenue for the analysis of protein interaction networks. A major challenge in systems biology is to understand the intricate network of interacting molecules. The complexity in biological systems arises not only from various individual protein molecules but also from their organization into systems with numerous interacting partners. In fact, most cellular processes are carried out by multi-protein complexes, groups of proteins that bind together to perform a specific task. Some proteins form stable complexes, such as the ribosomal complex that consists of more than 50 proteins and three RNA molecules, while other proteins form transient associations and are part of several complexes at different stages of a cellular process. A better understanding of this higher-order organization of proteins into overlapping complexes is an important step towards unveiling functional and evolutionary mechanisms behind biological networks.bait protein, either directly or indirectly, and are thus suited to identify multi-protein complexes. In fact, several research groups have systematically applied TAP/MS technology to study protein complexes involved in different signaling pathways [Data on protein complexes are collected from the study of individual systems, and more recently through high-throughput experiments, such as yeast two-hybrid Y2H) ,2 and taH ,2 and pathways .et al. [functional module, a group of cellular components and their interaction that can be attributed a specific biological function. The authors also suggested the modular organization of molecular interaction networks, where each functional module involves a small number of cellular components and is autonomous, i.e., its interaction with other modules is limited to a few cellular components. Subsequently, this assumption was used in several computational methods to identify protein complexes and functional modules in high-throughput protein interaction networks [cliques or "cliquish" components. For example, Spirin et al. [Protein interactions are routinely represented as graphs, with proteins as nodes and interactions as edges (links). Therefore, it is not surprising that analysis of protein interaction networks reach out for a variety of graph-theoretical tools. Following the observation that protein interaction networks display a characteristic power-law like node degree distribution , a substet al.  introducnetworks -15. Somenetworks -13 look n et al. ,15 combiet al. applied modular decomposition to elucidate the organization of protein complexes [prime if it cannot be decomposed any further. Only graphs that belong to a very special graph family called cographs can be completely decomposed . While the modular decomposition provides an excellent description of combinatorial variants within a family of complexes, it does not impose any order on the complexes within the family. As such it lacks the description power to represent the dynamics of complex formation, i.e., the manner in which proteins form transient interactions to participate in the complexes within the family. The order imposed on protein complexes within the family is particularly interesting if the family corresponds to a functional module where biological function is achieved through a dynamic formation of protein complexes and the order reflects this formation.In a recent paper, Gagneur omplexes . The basfunctional groups. A functional group is either a maximal clique  or a set of alternative variants of such complexes/cliques. As components of a larger functional module, functional groups are not assumed to be well separated and can have significant overlaps. Intuitively, if a functional module performs a function that requires a sequence of steps  then we would like functional groups to be snapshots of protein associations at these steps. We propose a new method for identifying and representing overlapping functional groups in a functional module. Furthermore, if the module corresponds to a dynamic process that requires certain complexes  come into contact in a specific order, our method attempts to discover this order. Our method is motivated by a fundamental result for chordal graphs [clique tree representation. However, not every protein interaction network is chordal and not every functional group is a clique. Therefore, we developed a graph-theoretical framework that enables automatic construction of a tree-like representation, analogous to the clique tree representation, for much broader family of graphs. We call this representation the Tree of Complexes representation. The nodes in the tree are functional groups, and for every protein, the set of functional groups that contain this protein forms a single subtree. The "single subtree" requirement restricts significantly the way in which the nodes of the tree can be interconnected. As a consequence, this representation shows a smooth transition between functional groups and allows for tracking a protein's path through a cascade of functional groups. Therefore, depending on the nature of the network, the representation may be capable of elucidating temporal relations between functional groups.In this work, we model a functional module as a union of overlapping dense subnetworks called here l graphs , which sComplex Overlap Decomposition (COD), that given a protein interaction network identifies its functional groups and constructs the Tree of Complexes representation. Our method requires that the network satisfies certain mathematical properties. We applied the COD method to several protein interaction networks, such as the TNFα/NF-κB signaling pathway and the pheromone signaling pathway. The corresponding subnetworks for all interaction networks are extracted from high throughput experimental data. Our results show that the COD method opens a new avenue for the analysis of protein interaction networks.We developed a new method, One way to represent a set of overlapping functional groups is to construct a graph with nodes representing functional groups and edges representing overlaps, i.e., there exists an edge between two functional groups if and only if they share at least one protein. This approach has two shortcomings. First, it is not obvious how to correctly identify functional groups, and second, such a representation does not provide any information about the dynamics of proteins in the network. We propose a graph-theoretical approach, which, under the assumption that the protein interaction network satisfies certain mathematical properties, identifies functional groups and provides a representation of overlaps between functional groups in the form of the Tree of Complexes.α/NF-κB and pheromone signaling pathway interaction networks.Here, we first describe the COD method. Then, we demonstrate the utility of our approach by applying the COD method to several examples, derived from high-throughput experiments, TNFchord in a graph is any edge that connects two non-consecutive nodes of a cycle. A chordal graph is a graph which does not contain chordless cycles of length greater than three.Our method of representing overlapping functional groups, which is depicted in Figure clique tree representation or clique tree [An important property of chordal graphs, which is explored directly in this paper, is that every chordal graph has a corresponding que tree . The nodExample Consider a hypothetical protein interaction network in Figure B through F. In the naive representation, the overlap between these maximal cliques is collapsed to a clique. Thus, the representation treats the maximal cliques and overlaps between them equally. In particular, there is no way to tell that, for example, D occupies a more central position in the network than B. In the clique tree representation this information can be extracted from the relative position of cliques in the tree. For example, B is connected to F by a path that passes through C and D, which means that any protein shared by B and F is also contained in C and D. In other words, the overlap between B and F is entirely contained in the overlap between B and D, which in turn is entirely contained in the overlap between B and C. Thus, there is a correlation between the amount of overlap between maximal cliques and their distance in the clique tree.While both representations show the overlap between maximal cliques, the interconnection pattern of cliques in the naive representation carries little additional information about the structure of this overlap. On the other hand, a very specific tree-like interconnection pattern in the clique tree representation can expose a special structure of such overlap. For example, consider maximal cliques Nice properties of the clique tree mentioned above make it a good choice for representation of overlaps between functional groups. However, not every protein interaction network is chordal and maximal cliques may not always be the best way to represent functional groups. For example, in Figure cograph can be characterized by an absence of an induced subgraph which is a path of length four (P4), where the length is the number of nodes in the path. Thus, the diameter of a connected cograph is at most two. Subsequently, connected cographs are dense and cliquish, consistently with the assumption made by algorithms that delineate protein complexes. What makes cographs even more attractive is that for every cograph there exists a Boolean expression which describes all the maximal cliques in the graph.  rather than maximal cliques. If we knew in advance all the functional groups in the module, we could simply connect the proteins within each functional group turning it into a clique and, under the assumption that the resulting graph is chordal, apply clique tree construction algorithm to the graph. Since we do not have predefined functional groups, our algorithm identifies them by adding edges to the graph in such a way that each added edge connects a pair of nodes that putatively belong to the same functional group.weak siblings. We call a pair of nodes weak siblings if and only if they are connected to the exactly the same set of neighbors, but are not connected to each other. In terms of protein interaction networks, weak siblings are proteins which interact with the same set of proteins but do not interact with each other. In particular, proteins that can substitute each other in a protein interaction network may have this property. Similarly, a pair of proteins that belong to the same complex but are not connected due to missing data or an experimental error will be represented as weak siblings. Since the weak siblings relationship suggests functional similarity, the COD method takes a first step towards delineation of functional groups by connecting every pair of weak siblings by an edge. As this modification may also eliminate some of the chordless cycles of length four (squares) in the graph, functional group delineation happens simultaneously with transformation of the protein interaction graph into a chordal graph.The COD method's edge addition strategy and its biological motivation builds on a concept of P4 in a maximal clique of the modified graph (cf. Methods);If, after connecting all pairs of weak siblings, the resulting graph is not chordal, the COD method attempts to transform it to chordal by adding some additional edges. Consistently with our assumption that we connect only nodes corresponding to proteins that could be put in the same functional group, we impose restrictions on this "fill-in" process. Namely, we require that, each introduced edge connects a pair of nodes which are close to being weak siblings. In such case the new edge is a diagonal of one or more squares in the graph. We emphasize that adding edges between nodes of longer cycles has no such justification. To summarize our edge addition procedure, our method attempts to eliminate all the squares in the protein interaction network by adding a limited set of diagonals that satisfies following conditions (i) connects potentially functionally equivalent proteins, as measured by the overlap in neighborhoods or distance from being a pair of weak siblings; (ii) ensures that functional groups correspond to cographs; we argue that this condition is guaranteed if the set of added edges does not form a If the modification step succeeds, i.e., the modified graph is chordal, all the clique tree representations of the modified graph are computed and then each clique tree is extended to a Tree of Complexes representation of the original graph. The COD algorithm keeps track of all the edge additions and uses this information to delineate functional groups by projecting each maximal clique onto original network and removing all introduced edges contained in the clique. For example, in the modified graph of Figure α/NF-κB signaling pathway. The Nuclear Factor κB (NF-κB) family of transcription factors is activated in response to a diverse set of stress stimuli, which includes pro-inflammatory cytokines, e.g., TNFα. In vertebrates, this family includes p50, p52, Rel A, c-Rel, and Rel B, which bind to the DNA in a homo or heterodimeric fashion. The NF-κB activity is regulated by the IκB family of proteins via inhibitory ankyrin repeat domains. This family includes IκBα, IκBβ, and IκB∈. The precursors of p50 (p105) and p52 (pl00) also possess ankyrin repeat domains and thus act as inhibitors. These precursors can also form dimers with other members of the NF-κB family. The activation with the pro-inflammatory cytokine tumor necrosis factor TNFα triggers a signaling cascade, which, in particular, stimulates the activation of the IKKα, IKKβ, and IKKγ functional groups. The IKKs initiate a signal induced degradation of the inhibitors (IκBs), and subsequent nuclear translocation of the transcription factor. Recent TAP experiments [et al. identified 221 molecular associations, out of which only 80 where previously known. Gagneur et al. [To illustrate the power of COD in elucidating the dynamics behind protein complexes, we consider the TNFr et al.  applied κB-inducing kinase phosphorylating IKKα and IKKβ) with at most three edges. For the purpose of the analysis, we contracted all five members of NF-κB family into one node. As the resulting protein interaction network, shown in Figure We used the COD method to analyze the subnetwork spanning all the paths from NIK  cascade and subsequent activation of nuclear proteins that control subsequent cellular events. In a recent paper, Spirin et al. [et al. and its Tree of Complexes representation is given in Figure MKKl and MKK2 .Then, to transform the network to a chordal graph, three additional edges are added: , , and . In this case, some functional groups will contain more than one protein complex.The yeast n et al.  identifin et al. . We analH can be connected to either B or C; (ii) any interconnection pattern that spans groups E, F, and G can be chosen. If we ask for a tree with maximum number of leaves, the number of tree variants is reduced to two (option (i)).This network admits six different Tree of Complexes representations: (i) functional group OR) in their functional group (H). The MAP kinases KSS1 and FUS3 are two separate kinases both activated by STE7 each of which is essential for a different program: FUS3 – for mating; KSS1 – for the filamentous growth [OR) in groups F and G. STE5 is a scaffold protein of the MAPK module. It recruits MAPK module kinases . This is consistent with the central position of a functional group containing STE5 in the tree and relative to the paths of STE7, STE11 and FUS3. Finally, nuclear proteins DIG1 and DIG2  enter at the endpoint (node F) in the tree.The MAPK cascade module consists of three sequentially acting protein kinases: MAP kinase kinase kinase (STE11) MAP kinase kinase (STE7) and MAP kinase  . MKKl ans growth . Once agRecent advances in experimental techniques resulted in the accumulation of a vast amount of protein interaction information, which is routinely represented by protein interaction networks. Therefore it is not surprising that increasingly more complex graph-theoretical tools are deployed to analyze protein interaction graphs and extract biologically meaningful patterns.In general, graphs are not required to have any type of regularity. This makes them a very flexible tool which is able to represent complex relationships. However, this often also makes them computationally hard to deal with, for many problems in graph theory are NP-complete. Frequently graph theoretical problems can be simplified if some restrictions are imposed on the graph. Various restrictions give rise to various graph families. Given a graph family, it is usually very useful to be able to represent it using some kind of a tree. Such tree representation exposes a hierarchical organization that a graph may have, allowing for a structured analysis of it.α/NF-κB signalling pathway this results in a unique Tree of Complexes representation and in the pheromone signalling pathway in two very closely related possible Tree of Complexes representations. The nature of high-throughput protein interaction data does not directly imply that this data encodes temporal relations. We demonstrated that our method is frequently capable of discovering such temporal relations. Interestingly, temporal associations can also be implicated in the absence of actual interaction in the data. For example, in the case of the pheromone signaling pathway, our method correctly included KSS1 and FUS3 in the same functional group , despite the fact that there is no link between KSS1 and FUS3 in the input protein interaction network.In this work we proposed a tree representation for protein interaction graphs called Tree of Complexes representation. Nodes in the Tree of Complexes are functional groups and the tree satisfies the additional condition that functional groups that contain any fixed protein form a connected subgraph. In this way, our representation captures not only the overlap between functional groups but, potentially, also the manner in which proteins enter and leave their enclosing functional groups. We developed a method  for efficient identification of such overlapping functional groups and construction of the corresponding Tree of Complexes. In particular, our method differs from other approaches in that it does not attempt to enumerate disjoint complexes but instead identifies and represents relations between overlapping functional groups. Even though the Tree of Complexes representation is not unique, the protein interaction networks that we analyzed admit very few alternative tree topologies. If we ask for a tree topology with a maximum number of leaves, as not to impose an artificial order between functional groups, the number of tree topologies is reduced even further. Thus, in the TNFObviously, there are limitations to deciphering such temporal relations. For example, we cannot provide temporal ordering between different tree branches. Furthermore, if a functional group is not a clique but is represented as a Boolean expression indicating various possibilities for such group, then one can not be sure if these variants are mutually exclusive or if they represent partial information capturing incomplete data. Even in the case when the functional unit forms a clique it is still possible that it contains interactions that are not simultaneous. For example, interactions between pairs ,  and  are represent as a three-vertex clique with nodes A, B, C and thus cannot be distinguished from a trimer . Such coincidences are less likely for larger cliques.Although our algorithm is not guaranteed to produce the Tree of Complexes representation for every possible protein interaction network, the algorithm will succeed for a broad family of graphs, which includes chordal graphs  and cographs. Currently, our method can be applied to protein interaction networks that do not contain long (longer than four node) chordless cycles. As a consequence, it is more appropriate for analyzing dedicated subnetworks or modules than large protein interaction networks, which are expected to contain such long cycles. We distinguish between two different types of problematic networks for our method. First type includes networks for which imposing a temporal order that encompasses all functional groups in the network is meaningless. Second type includes networks for which such order is meaningful, but the assumption that the overlap between functional groups has a tree-like structure is not valid. We plan to extend our approach to deal with networks of the second type by utilizing graph-theoretical tools developed for other specialized graph families, such as arc-intersection graphs.Another issue that requires further investigation is the presence of noise in high-throughput protein interaction networks and its effect on the Tree of Complexes representation. While our method deals to some extent with false negatives, through its edge addition procedure, the issue of false positives is not addressed. We plan to explore alternative graph modification procedures that will incorporate both false negative and false positive interactions.We use an elegant and efficient strategy for chordal graph recognition outlined in . We use P4 formed entirely by the added edges. Following lemma guarantees that every functional group is a cograph and therefore admits a compact Boolean representation.Recall that a functional group corresponds to a maximal clique in the modified protein interaction network, with modifications being addition of edges between every pair of weak siblings and then addition of edges that eliminate all the squares in the graph. The edge addition is such that no maximal clique in the modified graph contains an induced Lemma For every functional group, a subgraph of the original graph induced by the members of the group contains an induced P4 if and only if the set of edges added by our algorithm contains an induced P4.Proof The argument follows from Figure v1, v2, v3, v4) is a P4 in the original graph if and only if  is a P4 formed by the added edges.k edges that eliminate all the squares in the graph.We use a reduction to the Minimum Vertex Cover problem to find all minimal sets of up to S = {e1,..., er}, is assigned a cost:Each eliminating set of edges, ei) takes values between 1.0 and 0.0, and measures our confidence in adding ei to the graph. Since the addition of ei =  implies an interaction or functional equivalence between proteins ui and vi, we chose sim(ei) to be the amount of overlap between the neighborhoods of ui and vi, i.e., sim(ei) = , where  (vi) denotes a set of neighbors of node vi in the graph. Intuitively, sim(ei) measures how close ui and vi are to being a pair of weak siblings. If ui and vi have the same neighborhoods then sim(ei) = 1.0; as the overlap between the neighborhoods decreases, sim(ei) goes to 0.0. Then, we pick an edge set with the minimum cost from all the edge sets that do not form a P4, which is entirely contained in one of the maximal cliques of the modified graph. The last requirement is necessary to ensure that each functional group is a cograph.where sim and . Note that  is not a square as one of its diagonals, , is an edge in the graph. The square  can be eliminated if either edge  or  is added to the graph. Furthermore, a single diagonal can eliminate more than one square. For example, the diagonal eliminates both squares. We are interested in finding all minimal sets of diagonals of size up to k that eliminate all the squares in the graph.A square in a graph can be eliminated by adding one or both of its diagonals (chords) to the graph. For example, a graph in Figure O(2k(n + m)) algorithm [k that eliminate all the squares in the graph.We reduce the above problem to the Minimum Vertex Cover problem. The squares in the original graph become edges and diagonals become vertices in the new graph. Thus the original graph is transformed to a "square coverage" graph, which in turn serves as an input to the Minimum Vertex Cover problem. In the Minimum Vertex Cover problem we are given a graph and are asked to find the smallest set of vertices that cover all the edges in the graph. An edge is covered if at least one of its end points is selected. Coming back to our example, it can be easily seen that {} is the minimum vertex cover Figure . Althouglgorithm  to identP4, which is entirely contained in one of the maximal cliques of the modified graph. It can be shown that the latter case is possible only when the original graph contains a P4, and thus is not a cograph. We conjecture that graphs that admit Tree of Complexes representation are exactly those graphs that admit a clique tree representation, with the nodes being maximal cographs rather than maximal cliques.The COD method is not guaranteed to produce the Tree of Complexes representation for every possible protein interaction network. How can a family of graphs that admit Tree of Complexes representation be characterized? First, we argue that chordal graphs belong to this family. It can be shown that addition of edges that connect weak siblings does not introduce chordless cycles to the graph. Therefore, after all weak siblings are connected the graph is still chordal and thus admits Tree of Complexes representation. Next, we argue that cographs admit Tree of Complexes representation. Our methods eliminates all the squares in the graph, unless every possible set of eliminating edges forms a The author(s) declare that they have no competing interests.EZ implemented the algorithm implementation, and participated in design of experiments, analysis of results, and drafting of the manuscript. KG and RJ participated in design of experiments, analysis of results, and revising of the manuscript. TP conceived the project and participated in the design of experiments, analysis of results, and drafting of the manuscript.


Text mining has spurred huge interest in the domain of biology. The goal of the BioCreAtIvE exercise was to evaluate the performance of current text mining systems. We participated in Task 2, which addressed assigning Gene Ontology terms to human proteins and selecting relevant evidence from full-text documents. We approached it as a modified form of the document classification task. We used a supervised machine-learning approach (based on support vector machines) to assign protein function and select passages that support the assignments. As classification features, we used a protein's co-occurring terms that were automatically extracted from documents.The results evaluated by curators were modest, and quite variable for different problems: in many cases we have relatively good assignment of GO terms to proteins, but the selected supporting text was typically non-relevant (precision spanning from 3% to 50%). The method appears to work best when a substantial set of relevant documents is obtained, while it works poorly on single documents and/or short passages. The initial results suggest that our approach can also mine annotations from text even when an explicit statement relating a protein to a GO term is absent.A machine learning approach to mining protein function predictions from text can yield good performance only if sufficient training data is available, and significant amount of supporting data is used for prediction. The most promising results are for combined document retrieval and GO term assignment, which calls for the integration of methods developed in BioCreAtIvE Task 1 and Task 2. Rapid advancements in biology, molecular biology and biomedicine have led to the development of a range of factual and experimental databases. Apart from structured data related to sequences, expressions, etc., the significant body of biomedical knowledge is stored in the domain literature. The size of textual archives is increasing so rapidly that it is impossible for any user to locate and assimilate new knowledge without automated help. In particular, efficient curation of biological databases relies essentially on the ability to search and manage published articles rapidly and cost-effectively. This fact has spurred huge interest in designing text mining methods that can help users in locating, collecting and extracting relevant knowledge represented in an unstructured, free-text format.The BioCreAtIvE evaluation was organised to assess the performance of current text mining systems in two tasks. Task 1 was protein name identification in free text  . Task 2 In this article we present our approach used within the framework of BioCreAtIvE Task 2. We participated in all three subtasks. We considered Task 2.3 as the most general one, and the other two subtasks as its nested problems , we approached the task as a knowledge discovery problem. More precisely, our approach aims at mining associations from a corpus even when an explicit statement  relating a protein to a GO term is not present (in a given set of documents). The approach follows an original and renowned example of "hidden links" presented by Swanson and Smalheiser -6 . We based our method on SVMs, which have been demonstrated to perform well at the document classification task , as we cAssignments of GO terms (both for learning and predicting) were based on collecting "weak" co-occurrence evidence within documents, rather than on explicit statement(s) of protein function. Therefore, an important facet of our approach was that GO assignments were not derived from a single, "relevant" passage or sentence, but from document(s) relevant to a given protein. Further, selection of supporting passages  was based on a similar idea. Each paragraph pertaining to a given protein was assessed with respect to a given GO term, and the highest scoring passage was selected. More specifically, the employed method involved three steps: a) pre-processing of documents and feature selection, b) training the SVMs on the released training data, and c) predicting GO terms and selection of paragraphs for target (testing) genes.experimental, methods and reference sections of each document body, as we considered that these were unlikely to contain information on protein function and might introduce unnecessary noise. We also removed non-textual elements (such as tables and figures), but retained figure legends, as they might have some useful information and clarification . The problem with sup tags (used for both superscripts (e.g. in names) and for marking references to footnotes) was ignored, i.e. these tags were removed and their content concatenated to the preceding text (which generated obvious problems in case of footnotes). SGML entities  were replaced with the appropriate ASCII equivalents , collected from available DTDs. Then, the whole corpus was POS tagged using a general-language tagger  from the Journal of Biological Chemistry was distributed by the organisers. Document pre-processing involved several steps. Firstly, we parsed the SGML documents to remove cf. also ). All rer . Further, the high neology rate of other relevant terms makes existing glossaries incomplete for dynamic searching, and thus automatic term extraction tools are needed for efficient term identification [Extraction of features used for protein classification (i.e. association to GO terms) was the next step. As features, we used automatically extracted terms  that co-occurred with a given protein within the same document. Since terms represent the most important concepts in a domain, we hypothesised that they might be useful features for the annotation task. Also, we have previously shown that using biological terms as classification features improves performance when compared to single words . Howeverfication .In order to automatically recognise terms in text, we used an enhanced version of the C-value method . The met+ N, where A and N denote an adjective and a noun respectively), which has proven to have the best precision/recall ratio [In order to suggest possible terms, the C-value method combines linguistic formation patterns and statistical analysis. The linguistic part includes extraction of term candidates by using a set of formation patterns, and a stop list to eliminate frequent false term candidates. Term formation patterns act as linguistic filters to a POS tagged corpus: filtered sequences are considered as potential realisations of domain concepts. For the BioCreAtIvE task we used the simplest term formation pattern ll ratio ,22 for tleukaemia and leukemia; amino-acid and amino acid) and inflectional variants . Each term candidate was then mapped to a normalised canonical representative (CR), and consequently we established an equivalence relation, where two term candidates were related if and only if they shared the same CR. The partitions of this relation are denoted as synterms (synonymous terms): a synterm contains various surface term representations sharing the same CR. Further, an acronym recognition and conflation module [NFKB factor and NF-KB are linked to nuclear factor kappa B), and these were also included in corresponding synterms . In order to conflate equivalent surface expressions, we carried out linguistic normalisation of individual term candidates . For Task 2, we extracted 1.4 million distinct synterms from the corpus, and stored them in a database .Finally, synterms were statistically analysed, and each set was assigned likelihood to represent a domain specific concept (for details see ). Termhoidf, [w for protein p is given byFor weighting synterm features, we used a form of inverse document frequency idf, ,26) that thatidf,Rp is a set of relevant documents for the protein p, fj(w) is the frequency of w in document j, and Nw is the global frequency of w in the whole collection of documents. Note that the assignment of relevant documents was based on the released training data for the learning phase, while for the prediction phase we used either pre-selected documents (tasks 2.1 and 2.2) or we employed a retrieval method (Task 2.3).where GO term, protein, document), meaning that assignment of the GO term to the protein is supported by the document, with 659 distinct GO terms (assigned to 755 proteins and supported by 638 documents). This means that we had on average only 2.82 examples per GO term. Therefore, for each GO term, we collected positive learning examples from all training triples with GO terms that matched or were descendant from the GO term concerned. For these examples, we collected relevant documents (as released in the training set) and corresponding term-features (found in these documents) were used as pertaining to the given GO term. The negative training examples were obtained by taking an equivalent number of examples (and assigned documents) from sibling GO terms and their children.In the learning phase, we trained SVMs for GO terms on term vectors formed from document-protein pairs from the initially released training data. For Task 2, the training data included 1858 triples of the form . Using the hierarchy of the GO, we were able to extend the original 659 GO classifiers to create the total of 1436 (which still covers less than 10% of nodes of the GO ontology).svm-light package [The SVM classifiers were trained using the  package , and supNote, however, that – while we had the training data for assignments of GO terms to proteins – there were no data provided for training on selection of relevant paragraphs (only whole documents were distributed). Therefore, we used the same SVMs for both problems (prediction of GO terms for proteins and selection of passages).The prediction of GO terms for a target protein was performed in two steps. First we created a feature vector for a given target protein by collecting all synterms from given/relevant document(s). For tasks 2.1 and 2.2, the set of relevant documents for a given protein was pre-selected (i.e. specified as part of the task), while for Task 2.3 we employed an ad-hoc retrieval method to obtain documents from the corpus (see below). In the second step, we tested the feature vector against respective GO classifier(s), and selected the GO term(s) associated with the top-ranked classifiers .A similar approach was employed for detection of relevant passages: each paragraph pertaining to a given protein (i.e. appearing in any of the protein's relevant documents) was formed into a term vector and tested against the relevant GO classifier(s). The highest scoring passage was assigned as pertaining to the protein/GO annotation. Note that here we did not consider "similarity" between paragraphs and a testing protein, i.e. the relevant paragraph was selected only as being related to the GO term in question (but the documents from which the paragraphs were taken were assumed to be relevant to the protein).More specifically, the testing procedures for each of the subtasks were as follows:protein, GO term) pair we tested all the paragraphs from the specified document against the corresponding GO classifier (i.e. classifier that corresponds to the given GO term), and the top-ranked paragraph was selected. However, note that we could select supporting paragraphs only for the GO terms that appeared in training examples or were on a path from a training GO term to the root. We generated two submissions for this subtask. In Submission 1, if we did not have the corresponding GO classifier trained, the testing example was skipped. In Submission 2, we used a classifier trained for a nearest neighbour GO term if the exact one was not available, in an attempt to improve recall. Informally, we climbed up through the GO hierarchy from the given GO term until we found an available classifier. More formally, the nearest neighbour node was selected as the lowest common ancestor (in the same branch) for the test GO term and the original training data, and it was used to select a relevant passage.For a given  pair obtained, we applied the procedures used in Task 2.1, Submission 1, in order to select a relevant paragraph . We also generated Submission 2, where we used  GO classifiers derived from a new training set composed jointly from the initially released training data for Task 2 and the test data for Task 2.1 . We reasoned that additional classifiers might improve recall, and since the test sets for tasks 2.1 and 2.2 were distinct, we believed this was a fair approach to obtain more training data. We re-trained the SVM classifiers (as explained above) with the new data, obtaining additional 582 GO classifiers . So, in Submission 2 we used the same methodology as in Submission 1, but with additional GO classifiers.For a given protein, we generated its feature vector from a specified, pre-selected document, and tested it against all available GO classifiers. We then selected GO terms corresponding to the top-ranked classifiers (the number of assigned GO terms was as required by the assessors). Then, for each . We implemented an ad-hoc retrieval approach that used a variant of the inverse document frequency weighting ,26. We sHigh" judgments were assigned when GO terms or proteins were highly related to the selected passage, and "low" judgements were assigned when there was no relevance. In "perfect" predictions, both the GO term and the protein were marked as "high". If a passage was generally related to a given GO term , the evaluators assessed such results as "general". In case of proteins, "general" marks were assigned to cases where the selected passage was not exactly relevant for a specific protein, but was relevant to the protein family or a homologue. We present the results for each subtask separately.Evaluation of the results was performed by database curators from the European Bioinformatics Institute (EBI). It is obvious that it was a huge challenge to define and apply a consistent and meaningful evaluation approach. In the accepted framework, the focus was mainly on "usefulness" of a selected passage for deriving a given GO annotation of a given protein. The passages were assessed both from the perspective of the relevance to the suggested GO term and the relevance to the protein in question. "th out of 21 submissions), but recall was ranked in the upper half (9th out of 21 submissions). For predictions marked as "general", precision of all participating systems was in the range 5–6%, with recall for our system in the top 6 (out of 21).There were 1076 test examples in this subtask. The results were modest  pairs) in this subtask. The results for this subtask were disappointing. In Submission 1  precision was only 2.6% (see Table th and 12th (out of 18 submissions) for "general" and "perfect" predictions respectively, while our system was 4th and 9th with respect to recall .There were 435 test examples  were evaluated. Therefore, the assessment of performance in Task 2.3 and comparison to subtasks 2.1 and 2.2 are limited. Still, the results for Task 2.3 were encouraging: on average, 50% of assigned GO terms and selected passages were deemed relevant for a given protein , and not from a single article as in subtasks 2.1 and 2.2. Still, the results of Task 2.3 were quite variable for individual proteins. For two proteins (Q9972 and P08247), precision of assigned GO terms was very high  pair . Also, an additional, non-trivial problem is automatic retrieval of relevant documents (Task 2.3). The results obtained in response to these problems are quite different: in many cases we have relatively good assignment of GO terms to proteins , but the selected paragraphs are typically non-relevant.To our mind, there were at least two separate problems that were part of Task 2: assignment of GO terms that describe protein function (tasks 2.2 and 2.3), and selection of a supporting passage for a given , rather than on a rare but explicit statement of protein function. Therefore, it is essential to obtain substantial data to support predictions . For example, Task 2.3 results show that – when several relevant documents are used to make predictions – significantly better performance can be achieved (e.g. compared to the Task 2.2 results). The average number of documents retrieved for each protein in Task 2.3 was 10, compared to a single document approach in Task 2.2. Thus, the results from Task 2.3 show that relevant latent information can be inferred from weak evidence when several documents are analysed, and that single documents are not always sufficient to automatically predict specific associations using the method we applied: assignments that have been derived from many documents were more reliable than assignments based on a single document. Further, while supporting documents improve precision of GO annotations, availability of training data can improve the overall performance  resulted in significantly improved precision and recall of predicted GO terms).protein, GO term) pair is a huge challenge, as – in particular for assignment of functional annotations – supporting information can be distributed over several paragraphs or documents. From a biologist's point of view, short passages often cannot give unambiguous assignment of function without domain knowledge gleaned from other sources. Existing resources, for example Swiss-Prot, typically provide whole documents as supporting evidence. Analogously, the BioCreAtIvE training data also indicated only relevant documents. Therefore, as training examples for supporting passages were not available, it was impossible to automatically learn characteristic features on the paragraph level, and, consequently, we used SVM-classifiers trained on the document level. For the method that we applied, an additional problem (for predictions) was that passages (i.e. paragraphs) were typically too short, and consequently they contained few features . Therefore, as methods that rely on collecting sufficient amount of weak information cannot capture evidence from short textual segments, we could not provide accurate selection of paragraphs. Also, as longer passages have more features, our approach typically suggests lengthy paragraphs, rather than short sentences. Still, a promising outcome  is that using more general GO classifiers for passage extraction does not decrease precision.Selection of a relevant passage to support a  is the most interesting and realistic problem, as it does not require pre-selection of documents and GO terms for annotation of a certain protein. While GO terms could be assigned to proteins using some non-textual data-mining methods (e.g. homology searching), "pre-selection" of (a single) relevant document(s) that is "guaranteed" to be relevant to both the protein of interest and the GO term to be assigned is a highly challenging task. If such documents are selected automatically, analysis (or discovery) of relevant GO terms has to be taken into account as part of the process (which then converges towards Task 2.3), while manual pre-selection of such documents by human annotators (in order to be used in an automated system) is non-realistic. We further believe that it is rarely the case that a single document can be guaranteed to be self-contained and relevant to both the protein of interest and the GO term to be assigned. When human annotators derive functional annotations from such documents, they would almost certainly make use of significant background knowledge. This knowledge needs to be "captured" by an automated system in one way or another. This can only happen if there is a sufficient number of relevant documents to be considered for prediction, or if some additional knowledge source is used.synaptophysin (P08247) and BRCA1-associated RING domain protein 1 or BARD1 (Q9972)), where relevant documents were retrieved with very high precision . However, retrieved sets for complex protein names were typically not accurate, as documents were rarely related to proteins in question. For example, retrieval precision was around 10% for Task 2.3 proteins P30153  and Q9BYW1 . We believe that the relevance of the retrieval sets significantly influenced the quality of predictions for the respective proteins (see Table Our experiments have confirmed that selection of relevant (or "suitable") documents for a given protein is not a trivial task , but a ysin P0827 and BRCDNA-directed RNA polymerase II, holoenzyme to the BARD1 protein (Q9972), although the documents in the protein's retrieval set did not contain an explicit statement of this relationship. Instead, the GO term was loosely linked to the BRCA1 protein in this set of documents, and using the co-occurrence of the two proteins (BRCA1 and BARD1) with other terms, we were able to mine the association between BARD1 and the GO term. The mined annotation was afterwards confirmed in an article with the explicit statement of the relationship [Results of Task 2.3 have also shown that – when a sufficient body of documents is available – we were able to mine annotations from texts even when statements of certain relationships have not been clearly or explicitly stated. In such cases, relationships among proteins and GO terms were typically "discovered" by using a transitive closure of co-occurrence features collected from many documents (similarly to Swanson's approach ). For extionship , but thitricarboxylic acid cycle implies that the protein is located in the mitochondria.This example illustrates that predictions  can be indeed mined and inferred from existing "hidden" and weak evidence that is present in literature, and not only from explicit statements. We believe that similar types of latent information are very common. For example, a statement of interaction between two proteins implies their cellular co-location; hence, knowing the location of one is sufficient evidence for location of the other. Further, some background knowledge can be used to infer additional associations. For example, evidence that a protein is involved in the There is obviously significant room for improving the methods that we used for Task 2. For example, we treated all terms extracted from text equally, but additional credits could have been given to particularly relevant features for the task in question (i.e. GO terms in case of Task 2) when found as co-occurring with proteins/paragraphs of interest. Also, as we approached the protein function assignment task as a modified form of the document classification problem, the role of a protein was "limited" only to pre-selection of documents that would be analysed . Once the documents have been selected, training and classification were performed without further protein "input", and we used a whole document as a context surrounding a protein in question, from which the features (both for training and prediction) came. As documents may be too wide as relevant contexts, we will experiment with classification features that are collected from a narrower context of a protein (e.g. inside a paragraph where the protein is mentioned), or when feature weights depend on the distance from the protein occurrences. Still, spotting a mention of a protein in a paragraph is to a great extent a problem addressed in BioCreAtIvE Task 1, and thus integration of successful methods from that task could be beneficial. Also, this could help in locating relevant passages from the perspective of relevance to proteins (in the current implementation we did not measure the relevance of paragraphs to proteins in question). Furthermore, we believe that combination of our approach and some type of assessment of "lexical" similarity (e.g. one presented in ) betweenonly on released training resources, and no other data was used . For example, we could have used a set of GO classifiers that we have previously trained on yeast data [Additional knowledge and data sources might also be useful in improving the methods that we used for Task 2. In the current implementation, we approached the task in a "closed" manner, i.e. we relied ast data , and incast data ). Also, Automatic extraction of concise information on protein function from literature is undoubtedly a task of great relevance and utility to molecular biologists. In our approach to mining protein function from text, we used SVMs and features derived from terms co-occurring with a given protein to assign GO terms. We approached the problem as a variant of document classification, where GO assignments were not derived from relevant passages, but from relevant documents.The evaluation of the results shows the capabilities and limitations of supervised machine-learning approaches in text mining. Firstly, they can yield good performance only if sufficient training data is obtained, and significant amount of supporting data is used for prediction. The results show that performance improves as the number of relevant documents to a particular protein increases, while the method works poorly on short passages and/or single documents. The main reason is that short textual units often do not contain necessary information to infer protein function without information from other sources. This implies that our GO assignments  may be largely accurate and with relatively good recall, but finding the relevant passage may be difficult. Apart from a significant body of training examples (which needs to include examples of relevant text passages as well – if they are to be selected), such methods need to incorporate either some background knowledge (e.g. in the form of an ontology or a semantic network), or to analyse a substantial quantity of relevant text to learn or acquire such knowledge, or both. In our case, since we do not use any additional domain knowledge to support predictions, our method requires several documents for each protein in order to be effective. Of course, retrieval of relevant documents is an additional challenge, mainly because of lexical variability and ambiguity of protein names. On the other hand, we have shown that this approach can "discover" some associations from text even when an explicit statement relating a protein to a GO term is absent. In that sense, we believe that machine-learning approaches are more suited for addressing knowledge discovery tasks.There is obviously space for future improvements and experiments as discussed in the previous Section. Despite moderate results, the BioCreAtIvE exercise was very valuable, in particular for the identification and clarification of user requirements and open challenges, and concrete progress on how best to evaluate and interpret the results of text mining. Further, distribution of correct testing data (in particular relevant supporting passages) will be one of the valuable results of the evaluation. Finally, one of the main lessons learnt is that the two BioCreAtIvE task should not be viewed as isolated problems: for a successful solution to BioCreAtIvE Task 2, successful methods from Task 1 will be of great help.


Protein interactions support cell organization and mediate its response to any specific stimulus. Recent technological advances have produced large data-sets that aim at describing the cell interactome. These data are usually presented as graphs where proteins (nodes) are linked by edges to their experimentally determined partners. This representation reveals that protein-protein interaction (PPI) networks, like other kinds of complex networks, are not randomly organized and display properties that are typical of "hierarchical" networks, combining modularity and local clustering to scale free topology. However informative, this representation is static and provides no clue about the dynamic nature of protein interactions inside the cell.e.g. 20%) compatible with the estimated crowding of proteins in the cell cytoplasm. Proteins or protein complexes are free to translate and rotate on the lattice that represents a sort of naïve unstructured cell (devoid of compartments). At each time step, molecular entities (proteins or complexes) that happen to be in neighboring cells may interact and form larger complexes or dissociate depending on the interaction rules defined in an experimental protein interaction network. This whole procedure can be seen as a sort of "discrete molecular dynamics" applied to interacting proteins in a cell.To fill this methodological gap, we designed and implemented a computer model that captures the discrete and stochastic nature of protein interactions. In ProtNet, our simplified model, the intracellular space is mapped onto either a two-dimensional or a three-dimensional lattice with each lattice site having a linear size (5 nm) comparable to the diameter of an average globular protein. The protein filled lattice has an occupancy  and we have compared the dynamics of complex formation in a two and a three dimensional lattice model.We have tested our model by performing different simulations using as interaction rules those derived from an experimental interactome of ProtNet is a cellular automaton model, where each protein molecule or complex is explicitly represented and where simple interaction rules are applied to populations of discrete particles. This tool can be used to simulate the dynamics of protein interactions in the cell. Cell structure and physiology is governed by the precise wiring of the protein interaction network in the cell. In principle, if we had quantitative information about the kinetic constants describing the association and dissociation of all pairs of proteins in a proteome, we could attempt to combine it with information about interactions with small molecules, membranes and nucleic acids to model an electronic cell. This in turn could be used to ask very specific questions in order to test our understanding of the mechanisms underlying cell organization and its dynamic response to external stimuli.This strategy is computationally very demanding and, in its general formulation, hindered by the limitations of current technologies, at least in a foreseeable future. Currently, the main limitation is the lack of appropriate experimental technologies to produce accurate data at a global genomic level. If precise and complete data are required for carrying out simulations that have biological relevance, then it is unlikely that we will achieve useful cell models in the near future. It is possible, however, that even partial, noisy and/or qualitative data would be sufficient to develop models that can answer low-resolution questions.Recent technological advances have offered the opportunity to accumulate extensive information about the protein interaction networks in several organisms. In particular the yeast interactome has been looked at from several perspectives and using complementary approaches -4. AlthoThese types of data are usually represented as a graph where nodes (proteins) are connected by edges if the proteins in question have been shown to interact. Similarly to many social and technological networks, protein-protein interaction (PPI) networks, are not random in many aspects, displaying properties that are characteristic of "hierarchical" networks combining modularity and local clustering to scale free topology .The graph representation is informative since it allows us to visualize all the possible connections between the proteins in a cell. However, it is not possible to extract some biologically relevant information such as the complexes that are preferentially formed in the cell under different conditions or their compositional distribution. Finally, the graph representation does not have spatial and temporal resolution since it delineates an average over several conditions and time steps. In other words graphs render the static dimension of the protein interaction network and do not capture the dynamic nature and the non-homogeneous spatial distribution of the interactions occurring in a cell.We present here the initial characterization of a cellular automata model that uses as input experimentally derived protein interaction graphs and, by treating explicitly each protein or complex, produces a full dynamic picture of protein complex assembly and disassembly within a stylized cell.in vivo in the four spatio-temporal dimensions.Although some recent reports, appearing in the literature, attempted to describe the dynamics of protein interactions -9, none Saccharomyces cerevisiae. Protein interaction databases store more than 40,000 interactions between yeast proteins 1 >> m2, on average the hitting complex pushes m2 whereas, if m1 << m2 and m1 hits m2, then m1 stops. In the case m1 ~= m2, the probability the push succeeds is 1/2. Note that this mechanism is applied recursively in situations where a colliding protein or complex m1 pushes m2 which in turn pushes m3 and so on.as a consequence, if mAt each time step new complexes can form and existing complexes can break down into smaller complexes according to experimental interaction rules provided as input to the model. Two proteins residing in neighboring lattice sites may interact and form a new complex depending on whether they are linked by an edge in the input interaction graph.binding sites corresponding to the six sides of the hexagon in the 2D model or to the six cube faces in the 3D version of the model. Therefore, during a simulation, a single protein can bind to, at most, six other partners. Proteins do not compete for binding to the same partner and association is only limited by the number of edges, 6 in both 2D and 3D models. However, to prevent the formation of very large complexes containing multiple copies of the same protein, we implemented the ad hoc rule that a complex may contain only one copy of any protein species. Binding is stochastic and the probability of forming a complex is a function of an association probability that is related to the association rate constant (kon) of the interacting protein pair. After association, a complex moves as a single entity. Since for most protein pairs the value of the association/dissociation constants is unknown, we set it equal to an average value that is the same for all proteins.Monomers have six etc.). A simulation step, with a lattice containing approximately 106 sites at a protein concentration of 20%, takes approximately 0.25 seconds on an Intel(R) Pentium(R) 4 Mobile CPU 1.60 Ghz.  that simplifies the definition of input data and simulations management.)The model has been implemented by using the C language for performance reasons and for maintaining full control of memory management that can be an issue for simulations of large lattices. The resulting code is portable and runs under any major operating system (Linux, Windows, MacOS X, MB has coordinated the development of the software and has developed the graphic interface.  FC has contributed to the ProtNet software and performed several simulations.  AF has written the ProtNet software.  CG has performed the analysis of several simulations.  MT has contributed the yeast network.  GC has conceived the project, coordinated it and written the manuscript.High confidence yeast interaction network.Click here for file


Protein-protein association is essential for a variety of cellular processes and hence a large number of investigations are being carried out to understand the principles of protein-protein interactions. In this study, oligomeric protein structures are viewed from a network perspective to obtain new insights into protein association. Structure graphs of proteins have been constructed from a non-redundant set of protein oligomer crystal structures by considering amino acid residues as nodes and the edges are based on the strength of the non-covalent interactions between the residues. The analysis of such networks has been carried out in terms of amino acid clusters and hubs (highly connected residues) with special emphasis to protein interfaces.A variety of interactions such as hydrogen bond, salt bridges, aromatic and hydrophobic interactions, which occur at the interfaces are identified in a consolidated manner as amino acid clusters at the interface, from this study. Moreover, the characterization of the highly connected hub-forming residues at the interfaces and their comparison with the hubs from the non-interface regions and the non-hubs in the interface regions show that there is a predominance of charged interactions at the interfaces. Further, strong and weak interfaces are identified on the basis of the interaction strength between amino acid residues and the sizes of the interface clusters, which also show that many protein interfaces are stronger than their monomeric protein cores. The interface strengths evaluated based on the interface clusters and hubs also correlate well with experimentally determined dissociation constants for known complexes. Finally, the interface hubs identified using the present method correlate very well with experimentally determined hotspots in the interfaces of protein complexes obtained from the Alanine Scanning Energetics database (ASEdb). A few predictions of interface hot spots have also been made based on the results obtained from this analysis, which await experimental verification.The construction and analysis of oligomeric protein structure networks and their comparison with monomeric protein structure networks provide insights into protein association. Further, the interface hubs identified using the present method can be effective targets for interface de-stabilizing mutations. We believe this analysis will significantly enhance our knowledge of the principles behind protein association and also aid in protein design. Protein association is implicated in cellular signal transduction, antigen-antibody binding, in the regulation of gene expression and in the functioning of a huge variety of other constitutive multimers, where the multimeric state is the biologically active state. Hence, extensive research has been carried out to identify and to understand the underlying principles of protein association and interactions. Some insights to such interactions at atomic level have emerged from the analysis of large number of high-resolution crystal structures. Such investigations involve the characterization of the geometrical, chemical, and the energetic features of the interfaces as explained in the various reviews -6. Speciresidues . They alresidues . De et ace types . Bahadur packing . There h packing . Wolynes packing . Further packing . HoweverEscherichia coli RNA polymerase[We have earlier carried out an analysis on a limited set of twenty homodimers to understand the principles of protein-protein interactions from a graph perspective . This anolymerase. The netolymerase. HoweverInteresting observations made from the present analysis on protein multimers include the fact that the strength of interfaces evaluated using the interface clusters and hubs identified by present method correlate well with the kinetic and thermodynamic parameters of complex formation evaluated experimentally. Further, the interface hubs identified here also correlate well with the experimentally identified hot spots on the basis of binding free energy. This result indicates that hotspots can be associated with interface hubs, the identification of which can be useful in rationally designing interface de-stabilizing mutants. Further, a comparison of the interface hubs to the hubs within the protein monomer and with the non-hubs at the interface show significant differences in the interface hub properties, such as the contribution of the charged interactions being considerably higher at the interfaces. The analysis of the interface clusters has also shown that the protein interfaces are as strong as or stronger than the protein cores in more than half the protein complexes considered in the dataset. Thus, the present algorithm has given a new perspective into analyzing protein structures in general and protein complexes in specific, which has shed light onto some of the factors involved in protein association.et al., and Dokholyan et al., [et al., [The concept of networks in biology has been explored in the areas of protein interaction networks, metabolic networks etc . The ide et al., -45 have [et al.,  and Gree[et al.,  have rep[et al., ,38. We h[et al., . In the [et al., . Howevermin) are connected by edges. Such graphs generated at various Imin values, have been analyzed in this section to understand the details of protein-protein interfaces at the network level. Specifically, (1) the analysis of the interface clusters (defined as distinct clusters of amino acid residues with contributions from more than one chain of the protein oligomer) and interface hubs (defined as amino acid residues interacting with five or more residues with at least one residue belonging to a different chain than itself) have been presented. (2) The strength of interface interaction, as measured from the clusters and hubs identified at different Imin values has been compared with the experimentally determined dissociation constants for known complexes. Finally, (3) the relevance of interface hubs to the stability of the oligomer is pointed out comparing some of the identified interface hubs with experimental results.A non-redundant set of 455 protein oligomers is used in this study. The oligomeric protein structures as a whole are represented as graphs, with each amino acid as a node and the strength of non-covalent interactions  between them determining the edges. Those amino acid pairs with interaction strength greater than a user-defined cutoff  showed a very good correlation  indicating that the clusters identified at Imin = 6% are a good representation of the oligomeric interfaces. Hence, all generic cluster analyses are carried out at this Imin. This correlation decreases with increase or decrease of Imin since higher Imins give specific strong clusters that fail to represent the complete interface and at lower Imin, the monomeric protein core also becomes a part of the interface cluster. The interface cluster composition at Imin = 6% also correlated very well with the residue composition obtained from δASA calculations  with preference for residues like Arginine, Histidine, Tryptophan, Tyrosine and Phenyl Alanine, though other residues are not left out. Such preferences have also been observed in several earlier interface analyses [Interface clusters have been identified and analyzed for the loss of accessible surface area, the interface cluster composition and strength of the interface clusters based on Ianalyses ,15,33,36min values. A plot of normalized size of the largest cluster  Vs Imin is shown in Figure min = 4%. . The lamin (6%), which do not merge at a lower Imin (4%), then it clearly indicates the presence of more than one patch in the interface. This is shown in Figures min = 6% and Imin = 4% respectively. The dimer forms two distinct interface clusters that are strong and independent at both Imin = 6% and at Imin = 4% without merging at the lower Imin. This indicates the presence of two separate patches at the interface. Thus, the present method of interface cluster analysis can provide information regarding the size, strength and the constitution of interfaces involved in protein oligomers.Apart from the interface strengths, the present study involving interface cluster analysis also enables us to identify the number of interacting regions or patches that constitute the interface. For example, if a protein dimer shows two interface clusters at a higher Imin = 6%), analyses of interface hubs are carried out from Imin = 0% to Imin = 4%, because, beyond Imin = 4%, we do not get significant number of interface hubs for statistically significant results. The residue composition of the interface hubs, identified at Imin = 0% and Imin = 4% are presented in Figure mins is also presented in the figure so as to compare the residue composition of the hubs in the interfaces and the non-interface regions. The values presented in the figure are percentage compositions with respect to the residue composition in the complete dataset.The interface hubs are defined as those residues, which interact with five or more residues, out of which at least one residue belongs to the other monomer. Unlike interface cluster analyses , unlike the non-interface hubs, where either the hydrophobic or aromatic residues or both are preferred ahead of Arginine at any Imin. This shows that the protein interfaces have major contributions from the charged amino acid residues. The preference of Arginine and charged interactions at the protein interfaces has also been shown by a few previous analyses [It is evident from Figure analyses ,17,33,36mins, they are not preferred as interface hubs at these Imins. Hence, there is marked difference in the residue preferences in interface clusters and the hub preferences at the interfaces.Another important observation that can be made by comparing Figure A2 see  and Figumin = 4% are presented in Table We have already seen the differences between the hub preferences in the interfaces and the non-interface regions from Figure min = 4%, shows a different profile . This Tmin = 0% and Imin = 4% are given in Table min = 4% with a very high percentage in the hub-hub interactions. However at Imin = 0%, the charged interactions still dominate the nonhub-nonhub interactions, where as the hydrophobic interactions dominate the hub-hub and hub-nonhub interactions at the same Imin with a very high percentage in the hub-hub interactions. Therefore, when the Imin is varied, the profile changes dramatically for the interactions involving the hubs (hub-hub and hub-nonhub), whereas there is no change in the overall profile in the nonhub-nonhub interactions. It is evident from Table min = 0% and 4% are concerned. This is also consistent with the residue preferences in the interface hubs shown in Figure min is increased from 0% to 4%. Further, as we move from nonhub-nonhub to hub-hub interactions at Imin = 4%, the charged+polar interactions including salt bridges as well as the aromatic-aromatic interactions increase, where as the hydrophobic interactions decrease. However the same at Imin = 0% shows an increase in hydrophobic and aromatic interactions and a decrease in charged+polar interactions and salt bridges. In all cases, the hub-nonhub interactions fall in the intermediate category between the hub-hub and nonhub-nonhub interactions. These statistics clearly show a distinct profile for the interactions involving the hub residues when compared to those of the non-hub residues at the oligomeric protein interfaces.The pair-wise residue interactions across the interface can be categorized into three with respect to the hub status of the interacting residues: (a) Hub-Hub (b) Hub-Nonhub and (c) Nonhub-Nonhub interactions. The percentage of the charged and hydrophobic interactions in these categories at Iic) and the total number of residues in these interface clusters (Nires) at different Imins along with the experimentally determined dissociation constants (Kd) are given in the table for the chosen complexes. The complexes with μM Kd are weaker complexes and the ones with nM Kd are the stronger ones. In general, we find that the number of interface hubs, number of interface clusters, number of interface cluster residues and largest interface cluster size are all higher for the nM Kd complexes than the μM Kd complexes at all Imins. This indicates that the interface clusters and hubs identified and the Imin values used in the present method are genuine and robust and are good indicators of the strengths of oligomeric protein interfaces.We have considered eight protein-protein complexes with known dissociation constants -52 and amins and compare with the ΔΔG (differences in the free energies of the mutant and the wild-type) of the specific mutants given in ASEDB. We have relaxed the hub detection criterion to nodes with greater than or equal to 4 edges similar to the previous section so as to obtain statistically significant results for comparison with the experimental results. Interface hubs are identified at Imin values of 0%, 2%, and 4% and have been characterized by the highest Imin value at which they appear as interface hubs . Since there are very few interface hubs at Imin > 4%, we have not considered these hubs separately in this analysis.Experimental results are available on the stability of interface mutants for some protein complexes . These hmin whereas, most of the mutations with ΔΔG ≥ 4 kcal/mol are interface hubs at Imin = 4% (though there are some hubs at Imin = 2% and Imin = 0%). The fraction of hubs at Imin = 4% with ΔΔG < 1 kcal/mol is insignificant and there is no mutation with ΔΔG ≥ 4 kcal/mol, which is not a hub. Mutations in the 1–2, 2–3 and 3–4 kcal/mol ranges, do show a combination of hub characters, which are however consistent over the range. In general, we find that the interface hubs obtained at higher Imin values (Imin = 4%) have higher ΔΔG values than the hubs obtained at lower Imin values (Imin = 0%) and the residues that do not form hubs at all. Hence, the interface hubs identified using the present method correlate well with the experimentally obtained ΔΔG values of the interface hot spots.Figure min = 6%, out of which, five have ΔΔG ≥ 4 kcal/mol and the other five have ΔΔG varying between 1 and 4 kcal/mol. None of these have ΔΔG < 1 kcal/mol. One of the residues in the Trypsin-BPTI complex (Lysine 15) is known to have a ΔΔG ≈ 10 kcal/mol [min = 8%. This happens to be the only residue with such a high ΔΔG value and also the only one to remain a hub even at Imin = 8%.Out of the already mutated residues, ten are found to be hubs even at Ikcal/mol  and thismin = 4% (84 in number). These have been listed in Table Surprisingly, a large number of the interface hubs identified by the present method in these complexes, have not been mutated (not shown in figure). These include quite a few strong hubs identified at IThe oligomeric protein structures have been represented as networks, with amino acid residues as nodes and the edges have been constructed on the basis of non-covalent interaction strength (ranging from a cutoff of 0% to 6%) between amino acids. The analysis is focused on characterizing the interface clusters and hubs.The interfaces have been characterized as strong, if the largest cluster in the protein appears at the interface at high (6%) interaction strength. Interestingly more than 50% of the complexes in the dataset exhibit such strong interfaces. The interface clusters identified and their amino acid composition correlate with those identified from previous studies as well as from δASA calculations.The composition and the connections of the highly connected interface hubs have been evaluated at varying interaction strengths and compared with those of the non-interface hubs. The interfaces show an increase in Arginine hubs and a decrease in hydrophobic hubs when compared to the non-interface hubs. The hydrophobic residues, though present in the interface clusters, do not contribute to the interface hubs. Further, the interface hubs make the usual interactions such as salt bridges, stacking interactions and hydrogen-bonds as well as unusual interactions such as Arginine-Arginine interactions. The hub and non-hub interactions at the interfaces also show specific profiles with the hub interactions being dominated with hydrophobic interactions at lower interaction cutoffs and charged interactions at higher interaction cutoffs, whereas the non-hub interactions are dominated with charged interactions at all cutoffs. More importantly, the cluster and hub identification procedure picks up all types of interactions in a consolidated way, giving a global view of the interactions at the interface.The interface clusters and hubs identified correlate well with the experimentally determined dissociation constants for known complexes indicating that we have a robust method of identifying the strength of oligomeric protein interfaces. Finally, the hubs at high interaction strength have been identified as hotspots by comparing the ΔΔG values from alanine scanning mutagenesis experiments. Several strong hubs that have not been mutated have been predicted to be hotspots and await confirmation from future experiments.The dataset consists of a non-redundant set of protein multimer (455 in number) structures with resolution better than 2 Å, obtained from the protein data bank . These pThe loss of accessible surface area upon dimerization/multimerization was calculated from the residue-wise accessible surface area of the multimeric proteins and that of their respective monomers, which were obtained from NACCESS . The mulnode in the graph and the connections between these nodes are the edges. A group of interconnected nodes is defined as a cluster and a cluster with at least one residue belonging to a different protein chain in the multimer is denoted as an interface cluster. Contact number is defined as the number of edges made by a node and those nodes with a contact number greater than 4 (unless otherwise specified), have been identified as hubs. A hub with at least one residue belonging to a different protein chain in the multimer is denoted as an interface hub.Protein structures have been considered as a network of interactions amongst amino acid residues. Each residue in a protein complex is considered as a α atom is taken) are considered. The interactions between the sequence neighbors however, have been ignored. The interaction between two residues i and j has been quantified as defined by Kannan and Vishveshwara [The non-covalent interactions between side chain atoms of amino acid residues  × 100Iij is the number of atom pairs belonging to the side-chains of i and j coming within a distance of 4.5 Å and N is the normalization value for the amino acid type, which has been evaluated previously from a non-redundant set of proteins and also correlates with the size of the residue [ij for cluster identification. The normalization value of the residue i is used to evaluate the interaction Iij, for hub detection. In the identification of the clusters, both the normalization values of residues i and j are required during Iij evaluation due to symmetric considerations during graph construction. We have tried different combinations of the normalization values in this case, like sqrt(Ni × Nj), (Ni + Nj)/2 and min. Since they give qualitatively very similar results, we use the lesser of the two values ) for cluster identification. However, for hub detection, such constraints are not there and hence we have used the normalization value of the residue i (Ni) whose hub character is being evaluated.where n residue . The lesmin and any two non-sequential ij pair, which has an Iij value that is greater than a chosen Imin value, is connected by an edge in the graph. Such a graph is referred to as a protein structure graph for a given interaction strength Imin. The protein structure graphs are generated for all the multimers considered in the dataset using an Imin range varying from 0 to 10%. Physically, a higher Imin indicates strong interactions between the connected residues and a lower Imin includes the weakly interacting residues as well. For instance, at Imin = 0% even a single atom-atom contact between the side-chains of two residues is sufficient to connect them by an edge in the protein structure graph and more contacts are required for connections at higher Imins. The interface clusters and hubs were identified and analyzed in these protein structure graphs at varying Imins. Finally, an Imin of 6% was chosen for interface cluster analyses due to better correlation with results from δASA and an Imin of 0% to 4% was chosen for interface hub analyses so as to obtain statistically significant number for analyses.We choose an interaction cutoff, referred to as Ith element in the matrix is either 0 or 1 depending on whether the two nodes (residues) are connected (interacting) or not, on the basis of the chosen Imin. The diagonal elements are considered as 0 since connections with self are avoided. The amino acid residues forming disjoint clusters (with minimum three residues in each) are identified from the adjacency matrix by using a standard graph algorithm (depth first search (DFS) algorithm [clusters of all the interacting residues in the protein structure, from which the interface clusters are selected.The protein structure graphs have been represented as an adjacency matrix, which is an N × N matrix, where N is the number of residues in the protein structure. Each ijlgorithm ). This gcontact number greater than 4 are detected as hubs, from which the interface hubs are identified. The hub definition is relaxed to a contact number equal to or greater than 4, while investigating the interface hubs of single multimeric complexes in detail, as given in Tables interfacial hub preferences of amino acid residues and the preferences of the residues with which these hubs interact are obtained and compared with similar properties of the non-interface hubs and non-hubs at interfaces, identified from the same data set.Similarly, the residues with mins, the clusters in the protein oligomers are obtained using DFS and the size of the largest cluster in terms of the number of residues constituting it is obtained at different Imins. This has been found to be a function of protein size and hence the size of the largest cluster is normalized with respect to the protein size and is plotted as a function of Imin. The largest cluster size decreases as the Imin increases and the largest cluster obtained at a higher Imin may or may not be present at the oligomeric interface. An analysis is made on all the proteins in the data set, to find out if the largest cluster is at the interface or not at Imin = 6%. This provides an idea regarding the strength of the oligomeric interface with respect to its monomeric protein core.When analyzing complex networks, one of the most common parameters used is the size of the largest cluster . Here, wKVB carried out the construction and analysis of oligomeric protein structure networks. SV devised the concepts and the formalism used in this study. Both authors contributed to the interpretation of results and the preparation of the manuscript.min = 4%, respectively. Two figures  are also provided as additional material in Two tables (Table A1 and Table A2) are provided as additional material see , giving Click here for file


Genome sequencing projects generate massive amounts of sequence data but there are still many proteins whose functions remain unknown. The availability of large scale protein-protein interaction data sets makes it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks. Although several existing methods combine multiple information resources, there is no study that integrates protein domain information and PPI networks to predict protein functions.The domain context similarity can be a useful index to predict protein function similarity. The prediction accuracy of our method in yeast is between 63%-67%, which outperforms the other methods in terms of ROC curves.This paper presents a novel protein function prediction method that combines protein domain composition information and PPI networks. Performance evaluations show that this method outperforms existing methods. Genome sequencing projects are generating massive amounts of sequence data, and the functional annotation of these sequences became one of the most challenging tasks, especially for the many proteins whose functions remain unknown. Traditional computational methods have utilized sequence features and machine learning algorithms to predict functions. In recent years, high-throughput technologies, such as yeast-two hybrid, have provided large scale protein-protein interaction data, making it possible to develop new function prediction methods based on protein-protein interaction (PPI) networks ,2.Existing protein function prediction methods based on PPI can be categorized into two classes: direct methods based only on the protein interactions and module-assisted methods . Direct Direct methods are based on the assumption that interacting proteins probably have identical or similar functions -7. This Vazquez et al  assignedInstead of predicting individual protein functions, module-assisted methods first identify functional modules in PPI networks and then assign functions to the proteins according to functions of the module members. These methods are based on previous observations that a group of cellular components and their interactions usually can be attributed to a specific function ,12,13. TAlthough several existing methods have combined multiple information resources, such as gene expression information, gene regulatory networks and PPI networks, none of them have yet integrated protein domain information and PPI networks to predict protein functions. This paper presents a novel protein function prediction method that uses protein domain composition and PPI networks. This paper first demonstrates that proteins having similar functions are often in similar domain contexts in PPI networks and then develops the protein function prediction method based on this observation. The method gives satisfactory results compared to several existing methods.Yeast PPI network data was obtained from DIP database . 4,389 pThe domain annotation information was retrieved from the PFAM database ,21. The Domains are basic functional units in proteins. Cellular functions are accomplished by the cooperation of many domains in proteins. Therefore, the PPI network was decomposed into the domain level to investigate protein functions in terms of domain. Figure The domain context similarity (denoted as f) is defined as:Where M is the number of domain types in the PPI network. Given proteins A and B, SA and SB are the sets of domains included in A's neighbors and in B's neighbors. The number of domain types in SA is a, while the number of domain types in SB is b. The intersection of SA and SB is S, containing s types of domains. C(M:s) denotes combinatorial numbers. The larger f indicates a greater probability that A and B share similar functions.For each GO term, there is a positive data set composed of present proteins, and a negative data set including absent ones. For example, GO:0009277 is used to describe 107 yeast proteins, so these proteins were treated as positive samples. Since some GO terms contains only a few proteins and other GO terms are too general, only GO terms containing 10-200 proteins were considered.Given a protein P with unknown function, in order to examine its function with regard to each particular GO term, the domain context similarities, f, between P and each protein in both the positive and negative data sets were calculated. The function annotation of the protein with the highest f value was then assigned to P.The 7-fold cross validation, which has been widely implemented in previous researches ,24, was Four frequently used measurement indices, accuracy, precision, recall and Mathew correlation coefficient (MCC), were used to evaluate the prediction performance. The Mathew correlation coefficient (MCC) was calculated to assess the prediction performance when the numbers of proteins in the positive and negative data sets differed significantly. MCC ranges from -1 to 1, a larger MCC indicating better prediction performance. For data with positive predictions, the real positives are defined as true positives (TP), while others are defined as false positives (FP). For data with negative predictions, the real positives are defined as false negatives (FN), while the others are defined as true negatives (TN). Then, the measurement indices are defined as:-15. The distributions of the similarity for sets A and B are shown in Figures The relationships between protein function similarity and domain context similarity in the PPI network were investigated based on the measurement indices. First, 1000 pairs of proteins were randomly extracted from one GO term with the domain context similarity, f, then calculated for each pair (denoted as set A). Secondly, another 1000 random protein pairs were generated using pair of proteins from different GO terms. Their f values are also calculated (denoted as set B). The two sets of similarities were then compared to demonstrate the positive significant relationship between functional similarity and domain context similarity. The results showed that set A has a mean similarity, f, of 9.23 compared to 0.46 for set B. Kolmogorov-Smirnov test showed that set A is significantly higher than set B with a p-value less than 10The method was then used to predict protein functions in yeast. GO terms were divided into 4 groups according to the number of proteins in each GO term. GO terms containing less than 10 proteins were excluded due to the lack of a satisfactory number of proteins for accurate predictions. GO terms including more than 200 proteins were also eliminated because the function annotations in these GO terms are usually too general. The results are shown in Table The prediction accuracies are between 63%-67%. The results show that the method has satisfactory robustness for various numbers of proteins within one GO term. As number of proteins increases from 10-30 to 100-200, the accuracy only decreases slightly, by about 4%. The phenomenon that accuracies decrease as number of proteins in the GO term increases can be attributed to the fact that functional annotations in larger GO terms are not as specific as in smaller GO terms. Fuzzy, general annotation information may affect the prediction performance. Further investigation is required to explain this observation. Besides, the recall is higher than the precision, demonstrating that false positive predictions are more common than false negative predictions.This method was then compared with existing methods based on the ROC curves. The three previously developed methods included in comparison are MRF , NeighboA new prediction method for protein function based on protein-protein interaction and domain context was presented in this research. Domain context similarity in the protein-protein interaction network was defined and used as in index for prediction. The underling principle of this method was that proteins tend to interact with each other via domain-domain interaction. So the high quality domain-domain interaction information may improve the prediction accuracy. Riley at al  developeThis research also suggests several future directions of research. First, domain context similarity measurements or prediction systems can be improved to reduce false positive predictions and boost accuracy. For example, the cutoff value for domain context similarity can be introduced to improve the accuracy and to deal with multiple function problems. Since the underlying rationale of this method is the domain-domain interaction, high-quality domain interactions can definitely contribute to the accuracy. As mentioned above, the newly developed domain interaction inferring method -28 can bThe availability of large scale protein-protein interaction data sets makes it possible to predict protein functions based on protein-protein interaction (PPI) networks. Several existing methods combine multiple information resources to predict protein functions. We present a novel protein function prediction method that combines protein domain composition information and PPI networks. Performance evaluations show that this method outperforms existing methods. The results are used to analyze the relationships between domain context similarity and protein function similarity, while this research may have potential future research directions.The authors declare that they have no competing interests.ZS, HC designed the study, collected study data, performed the analysis, prediction and cross validation and produced the first draft of the manuscript. KL provided assistance in the process of collecting data and revising the manuscript. ZS was involved in designing the study and revising the manuscript. All authors read and approved the final manuscript.