The identification of protein-protein interaction sites is a computationally challenging task and important for understanding the biology of protein complexes. There is a rich literature in this field. A broad class of approaches assign to each candidate residue a real-valued score that measures how likely it is that the residue belongs to the interface. The prediction is obtained by thresholding this score.et al. (2007). In fact, any 3D-neighborhood relation can be modeled. On grounds of a generalized Viterbi inference algorithm and a piecewise training process for pCRFs, we demonstrate how to utilize pCRFs to enhance a given residue-wise score-based protein-protein interface predictor on the surface of the protein under study. The features of the pCRF are solely based on the interface predictions scores of the predictor the performance of which shall be improved.Some probabilistic models classify the residues on the basis of the posterior probabilities. In this paper, we introduce pairwise conditional random fields (pCRFs) in which edges are not restricted to the backbone as in the case of linear-chain CRFs utilized by Li PlaneDimers compiled by Zellner et al. (2011), from the list published by Keskin et al. (2004) and from the very recent data set due to Cukuroglu et al. (2014). That way we demonstrated that our pCRF-based enhancer is effective given the interface residue score distribution and the non-interface residue score are unimodal.We performed three sets of experiments with synthetic scores assigned to the surface residues of proteins taken from the data set PresCont server devised by Zellner et al. (2011) on PlaneDimers.Moreover, the pCRF-based enhancer is also successfully applicable, if the distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the http://ppicrf.informatik.uni-goettingen.de/index.html.Our results strongly suggest that pCRFs form a methodological framework to improve residue-wise score-based protein-protein interface predictors given the scores are appropriately distributed. A prototypical implementation of our method is accessible at  Protein-protein interactions are constitutive of almost every biological process. The ability to identify the residues that form the interaction sites of these complexes is necessary to understand them. In particular, it is the basis for new therapeutic approaches to treat diseases , 2.et al.n.In general, observations PlaneDimers, we follow n is the observation associated with the protein under study, and if Our pCRF-based enhancer utilizes one position characteristic and two edge characteristics on the basis of the standard step function method explained in the Methods section. If To enhance predictions obtained by thresholding, solely information coming from the residue neighborhood relations on the surface is additionally used.area under the ROC curve (AUC) for our pCRF-based enhancer on synthetic scores, we proceed as follows. For each edge Φi,j by κΦi,j, where κ∈.In order to be able to calculate the performance measure of et al.. The decisive factor for this choice is that the PresCont score distributions for interface sites as well as non-interface positions above θ are “sufficiently close to” unimodal distributions. For every such θ, we set all scores less than or equal to θ to zero and then left the classification of all surface residues to the pCRF modified as follows. The residues of score zero are not taken into account when it comes to discretizing the protein characteristics ∈{I,N}2, we replace Φi with exp), and Φi,j}{ with exp).First, for every position Second, we replace sums with products and then maxima with sums.Thus we obtain as analogues of the Viterbi variables i∈{1,2,…,n}, respectively, then the forward variable at stage i with respect to a boundary assignment If i>1), the forward variable i−1. Analogous to the linear-chain case, a random backward walk through a state graph, with all possible assignments i=n,n−1,…,1) being the set of nodes, results in a random labeling of the positions, where each labeling is drawn with its posterior probability.For any assignment This sampling technique allows the efficient calculation of posterior probabilities at nodes and edges in a straightforward manner.Residue-wise score-based threshold predictors of protein-protein interaction sites assign to each residue of the protein under study a score. The classification is then made by thresholding the score. In case of using probabilistic data models, the parameters of the threshold predictor have been learned on a training data set in advance.PlaneDimers compiled by Zellner et al.[et al.[et al.[We have demonstrated that such threshold predictors can be improved by pCRF-based enhancers given the shape of the interface surface score distribution and the non-interface surface score distribution with respect to the training set resemble the shape of unimodal distributions. Besides the surface residue scores, only the spatial neighborhood structure between the surface residues of the protein under study is taken into account. Thus, the improvement can be attributed to our model. In addition to the precision of the scores, the amount of improvement depends on the 3D-complexity of the interfaces to be predicted. To this end, three sets of experiments with synthetic surface residue scores for protein complexes randomly chosen from the data set r et al. and from[et al. and Cuku[et al..not influence the classification of its neighbors. When using pCRFs, this is the case.The enhancement is structurally based on the following model property of pCRFs in contrast to residue-wise predictors. Though the scores of near-by residues may be correlated, labeling a position as interface or non-interface by thresholding the score does PresCont server devised by Zellner et al. on PlaneDimers[The pCRF-based enhancer is also applicable, if the score distributions are only unimodal over a certain sub-domain. The improvement is then restricted to that domain. Thus we were able to improve the prediction of the neDimers.The prediction is made on grounds of a generalized Viterbi inference heuristic. As for training, we developed a piecewise training procedure for pCRFs, where the enhancer needs to be trained on data originating from the same source as the training data of the threshold predictor to be improved.http://ppicrf.informatik.uni-goettingen.de/index.html.A prototypical implementation of our pCRF-based method is accessible at


The proposed method is adaptive to protein complexes with different structure, number, and size of nodes in a protein-protein interaction network. Based on different complex sets detected by various algorithms, we can obtain different prediction sets of protein-protein interactions. The reliability of the predicted interaction sets is proved by using estimations with statistical tests and direct confirmation of the biological data. In comparison with the approaches which predict the interactions based on the cliques, the overlap of the predictions is small. Similarly, the overlaps among the predicted sets of interactions derived from various complex sets are also small. Thus, every predicted set of interactions may complement and improve the quality of the original network data. Meanwhile, the predictions from the proposed method replenish protein-protein interactions associated with protein complexes using only the network topology.A method for predicting protein-protein interactions based on detected protein complexes is proposed to repair deficient interactions derived from high-throughput biological experiments. Protein complexes are pruned and decomposed into small parts based on the adaptive  Protein-protein interactions (PPIs) contribute to the interpretation of cellular organization, processes, and functions. They also compose bigger molecules and protein complexes to perform molecular functions . The depOn the other hand, the known PPIs compose protein interaction networks. Many approaches are designed to predict PPIs and protein complexes based on the topology of the PPI networks . They enProteins in protein complexes tend to interact with each other . There ak-core which is a complete subnet composed of k nodes is used as a seed to disintegrate a complex with extension and pruning. The k-core is the local structure of topology and can ensure the dismemberment of various detected complexes. A protein complex is decomposed and pruned into several small subnets connected tightly by extended k-cores and the proteins in the complex of loose connections that are abandoned. Finally, the proteins in the subnets can be predicted to interact.According to the different structures of protein complexes, it is more feasible to predict the PPIs based on the densely partial subnets in the complexes, which always include areas of tight connection regardless of the differences in the size, number, and distribution of the topological structure. Therefore, we intend to disassemble the complexes when searching for the dense regions. A  Saccharomyces cerevisiae. The predicted PPIs are evaluated using a statistical method based on the gold standard [To validate the proposed methods of predicting PPIs, we choose three complex detection algorithms which found complexes highly different in the number, size, and topological distribution based on the DIP dataset fromstandard , and thestandard , which ck-cores method and its improvement in a special condition. In The remainder of this paper is organized as follows. In k-cores method to dissemble these subnets into smaller parts in which the proteins connect with each other more tightly. Proteins within smaller subnets are predicted to interact if they do not interact with each other in the original network. Finally, the performance of predicted PPIs is estimated using two ways.In this section, we will present the method of predicting PPIs in three steps and introduce the methods of estimating the predicted PPIs see . There aThis paper selects three algorithms for detecting the protein complexes and finding the initial subnets in a PPI network, that is, MCODE , COACH , and NDCk-core in it is determined randomly as the extended seed firstly. And then the seed is expanded in the region of the complex until the set conditions cannot be satisfied. The subnet based on extended k-core is pruned from the complex. The nodes of the rest of the complex are treated as a new complex. Finally, the above process is performed repeatedly until no k-core is found and the nodes of the rest of the complex are abandoned.For a detected protein complex, a k-core. A node in the detected complex that has the maximum number of connections with the k-core is chosen and appended into the k-core. The density of the extended k-core is calculated by density = 2 m/n(n − 1), where m and n are the number of edges and nodes in the extended k-core, respectively. If the density is larger than a threshold λ, then extended k-core is set as the new seed and continues to be expanded. Otherwise, the expansion stops. See The greedy method is introduced to extend the seed of the n protein nodes, m protein pairs, and l k-cores. Finding a k-core in the complex is O(nml) [k-core, finding a node that has the maximum number connected with the k-core is O(n). Calculating the subnet density is O(n2). Predicting PPIs based on the subnets decomposed from the complex is also O(n2). Therefore, the final time complexity is O(nml + n2). In practice, the number of nodes in the protein complex, n, is not too large, and it will decrease after an extended k-core is found. Thus, the real processing time is very short.The time complexity of the proposed method is analyzed as follows. For a protein complex, there are s O(nml) . In the  abcd that can extend node e.  abcde is pruned and the rest of the subnet fghi is not extended and is therefore abandoned. Consequently, only one subnet abcde is obtained to participate in the PPI prediction from the complex, and the other parts of the complex are discarded.h. The initial value of h is one for every predicted PPI. If a predicted PPI is to be predicted again by another subnet, the value of h is increased by one for the corresponding PPI. A high h corresponds to multiple times for a predicted PPI with different complexes and can present a better possibility for the reliability of the predicted pairs.There may be overlapping nodes among various protein complexes derived from a certain complex detection algorithm, such as algorithms COACH and NDComplex. The extended cores of COACH may have overlaps but its section is small. The overlaps among complexes derived from NDComplex are relatively large. So, various complexes are decomposed into small subnets and may predict the same PPIs. Therefore, we introduce a parameter of repetitive prediction, Pi and Pj to determine whether the interaction PiPj can be put in the predicted set  if the subnet contains n proteins.A set of original interactions is built based on the PPIs of the network and is defined as the original set. A set of predictions is used to store the predicted PPIs and is defined as the predicted set which is initialized with null. We traverse every protein in a subnet and test the arbitrary proteins  set see . SimultaL [L = (P+/G+)/(P−/G−), where P+ is the number of predicted PPIs contained in the true positive GS set, P− is the number of predicted PPIs in the true negative GS set, G+ is the number of the true positive sets of GS, and G− is the number of the true negative sets of GS. G+ and G− are constant and equal to 8250 and 2705844, respectively. The predicting performance is good if most of predictions hit in the true positive set and a few of predictions drop in the true negative set. This method can overcome the biased assessment from the deficient samples between positives and negatives. Jansen et al. have set two thresholds of L, that is, 300 and 600 [L of predicted PPIs is acceptable if L is more than the two thresholds. Of course, the larger L is the better.Two methods are proposed to estimate the predicted PPIs. The first estimation is a statistical method based on a likelihood ratio L . In this and 600 . The val Saccharomyces cerevisiae from primary literature [The second way of the estimation is via a direct comparison with other records of biological experiments. There are many datasets from various biological experiments for yeast. Database BioGRID collects sufficient and reliable data interature . TherefoAmong the methods of predicting PPIs derived from dense regions of PPI network, clique methods have the strictest topology so that they can obtain good performance of the PPI prediction. Therefore, we compare our methods with the clique methods and hope to obtain similar performance. The analysis of the predicting performance is presented in F-measure of detected complexes is achieved in the threshold range between 0 and 0.1 [t, c, d, and s. The first two represent the similarity threshold and the occurrence threshold during the computation of neighborhood density, respectively. The last two represent the subnet density in low and dense regions, respectively. They are set to 0.3, 3, 0.7, and 0.2 sequentially to get the best overall performance [Our method of predicting PPIs associated with complexes is applied to a large-scale PPI network from the DIP dataset of yeast (version of 2010/6/14) . DIP is  and 0.1 . Thus, tk-core to our method, that is, 4, 4, and 7, respectively. Generally, a subnet in a PPI network can be judged as a dense one when its density threshold exceeds 0.5 [h > 1, and this set is denoted by N+. The detailed prediction sets of M, C, N, and N+ are listed in the Supplementary Material (available online at http://dx.doi.org/10.1155/2015/259157).According to the scales of detected protein complexes from the three complex sets, we set three sizes of the eeds 0.5 . We adopeeds 0.5 . And we Meanwhile, we introduce two approaches of predicting PPIs based on cliques, Yu et al.  and YangL of statistical significance, and hitting ratio of BioGRID validation, respectively. The number of N that is close to the number of ALL is the largest at 928, and the number of M is the smallest at 171. The others are close to each other. These predicted numbers are consistent with the numbers of protein complexes derived from various complex detection algorithms. All of the L values of predictions are acceptable based on the low standard. Most of them are close to YU, except for the predictions based on COACH set. The value L of the predictions based on algorithm COACH is the lowest. The complexes from COACH are large, and the distribution of nodes in complexes is prone to be very loose. This illustrates that the large and loose structures of complexes are not conductive to predicting PPIs. All of these predictions have the relatively high percentage of hitting in BioGRID. Although the protein complexes derived from different complex detection algorithms are diverse in their topological structure, our method of predicting PPIs based on them is stable and the performance is close to the methods of Yu and Yang.The performance of various predicted PPIs is shown in λ between 0.5 and 0.9 to test the performance of the predicted PPIs  [ complex. The predicted protein pairs from different prediction sets related to complexes are about one-third  . We idenhird see . Because+, respectively. There are six types of protein complexes in the four collections of top 10 rankings, including proteasome complex, U4/U6 × U5 tri-snRNP, spliceosomal complex, transcription factor TFIID complex, proteasome core complex, and mRNA cleavage and polyadenylation specificity factor complex. The PPIs from different predictions focus on different complexes that are associated with the complex detection algorithms. The number of predictions between C and N+ is almost the same, but the hitting ratio in terms of protein complex has a wide gap. This is because the complexes derived from COACH are large and loose in the structure and some proteins in them are not likely to be in the same complex.k-cores can identify the dense regions in complexes despite the topological structure of the complexes. This paper proposes a method of predicting PPIs that is adaptive to various complexes robustly and the predictions are reliable with the estimations. The predictions based on various complexes detected with different algorithms can complement each other, and they differ from the ones derived from the clique methods. Therefore, the predicted PPIs can supplement the deficient data of the protein interaction networks associated with the protein complexes. The improved networks contribute to detecting the protein complexes and studying the relationship of proteins in complexes.Various protein complex detection algorithms produce complexes having different features in terms of the number, size, and distribution of the nodes. Nevertheless, the method of decomposing complexes based on The Supplementary Material is described as follows. “The Supplementary Material contains four PPI sets which are predicted based on four groups of protein complexes derived from various complex detection algorithms.


Saccharomyces cerevisiae and the experimental results show that DPC outperforms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment based on the validation of matching with known complexes and hF-measures.Identification of protein complexes from protein-protein interaction networks has become a key problem for understanding cellular life in postgenomic era. Many computational methods have been proposed for identifying protein complexes. Up to now, the existing computational methods are mostly applied on static PPI networks. However, proteins and their interactions are dynamic in reality. Identifying dynamic protein complexes is more meaningful and challenging. In this paper, a novel algorithm, named DPC, is proposed to identify dynamic protein complexes by integrating PPI data and gene expression profiles. According to Core-Attachment assumption, these proteins which are always active in the molecular cycle are regarded as core proteins. The protein-complex cores are identified from these always active proteins by detecting dense subgraphs. Final protein complexes are extended from the protein-complex cores by adding attachments based on a topological character of “closeness” and dynamic meaning. The protein complexes produced by our algorithm DPC contain two parts: static core expressed in all the molecular cycle and dynamic attachments short-lived. The proposed algorithm DPC was applied on the data of  In the postgenomic era, more and more attention has been paid to proteomics. Proteins are central part of life activity. Within a cell, proteins cannot work alone to carry out cellular functions while these cellular functions are performed by many proteins bound together into protein complexes . With thk-score and cohesion.Up to now, many computational methods have been proposed for identifying protein complexes from PPI networks. The most common network-based methods are to detect dense subgraphs from PPI networks as complexes for the researchers believe that proteins in the same complex generally implement the same or similar function and tend to interact with each other , 3. SpirBesides these density-based algorithms, there are some algorithms available to detect protein complexes based on other topological structure. Girvan and Newman  develope Scaccharomves cerevisiae from the data of Gavin et al. 2002 [ Saccharomyces cerevisiae generally contain a core in which proteins are highly coexpressed and share identical functional classification and cellular localization. The protein-complex core is often surrounded by some attachments, a functionally mixed group of proteins, which assist the core to perform subordinate functions. Gavin et al. [Recently, some researchers investigated the inherent organization of protein complexes on the basis of topological analysis. Dezso et al.  studied al. 2002  and Ho en et al.  providedn et al.  and Leunn et al.  exploredn et al.  detectedn et al.  proposedThe investigation of inherent organization of protein complexes provides a new clue to identify protein complexes from PPI network. There is a common perspective that proteins in the core tend to interact with each other and are generally highly coexpressed. Dezso and Gavin's work shows that attachments are often short-lived proteins. From this perspective, the protein complex discovery methods based on static PPI networks have limitations. In reality, cellular process is dynamic; proteins and interactions between proteins vary with time. Saccharomyces cerevisiae. The experimental results show that DPC can predict more accurate cores or complexes than COACH [Considering that gene expression data provide the dynamic expression information for each protein in the cell cycles –26, we pan COACH  and Corean COACH  and outpan COACH , MCL [27an COACH , and HC-an COACH  on the vDifferent from traditional network-based Core-Attachment methods, the proposed algorithm DPC develops new strategies to identify protein-complex cores and their attachments. The proposed algorithm DPC operates in three phases: detecting protein-complex cores, generating potential dynamic complexes, and filtering false positive complexes. Before the details of the proposed algorithm DPC are introduced, some related definitions are given first.G = , where V represents the set of proteins and an edge  ∈ E if and only if protein vi is found to interact with protein vj. Given such a graph G, the degree of a node v ∈ V, marked as deg(v), is defined as the number of neighbors of v in G. Let Nv denote the set of neighbors of v; deg(v) = |Nv|. A protein-protein interaction (PPI) network is modeled as an undirected graph C⊆G, let VC represent the set of proteins in C and EC denote the set of interactions between two distinct proteins in C. The density of C, denoted as den(C), is defined as the number of edges in it, divided by the number of potential edges; that is,For a given subgraph v, its gene expression profile can be abstracted into Ge(v) = {g, g,…, g, …, g}, where g denotes the expression value of protein v in kth time course. A protein is said to be active in kth time course if its g is larger than or equal to a given threshold Tg. In this paper, Tg is set as 0.7 which was typically used in previous studies [ always active proteins. PA is used to describe the set of the always active proteins. For a given protein v ∈ PA, NvPA is used to represent the subset of v's neighbor proteins which are always active in the cell cycle; that is, NvPA = Nv∩PA. The rest neighbor proteins of the node v are collected into a set which is marked as NvR. It is obvious that Nv is the union of NvPA and NvR; that is, Nv = NvPA + NvR.For a protein  studies . The proIt has been shown that a protein-complex core is a small group of proteins which are highly coexpressed, share high degree of functional similarity, and have more interactions between themselves . The proC⊆G is called a protein-complex core if it satisfies the following two constraints: (1) all its vertices are always active proteins, that is, for any vertex v ∈ C it is a vertex of PA and (2) it is a dense subgraph of G  and the density threshold Td is set as 0.7 in this paper according to the typical threshold value used in previous works [According to these properties of protein-complex cores, we first give its definition. A subgraph us works , 22).Based on the above definition, detecting possible protein-complex cores is divided into two main steps: searching always active proteins and forming possible protein-complex cores. Forming possible protein-complex cores is aimed to group always active proteins into many connected subgraphs according to the topological and dynamic features of protein-complex cores. The description of subroutine for detecting possible protein-complex cores is shown in v ∈ PA, its neighbors which also belong to PA are called its always active neighbors. The core candidate proteins are sorted by the numbers of their own always active neighbors in nonincreasing order. If two vertices u and v have the same number of always active neighbors , they will be secondly sorted by the numbers of their rest neighbors in G in descending order. The sorted always active proteins will be stored into a queue Q. The first vertex in the queue Q is picked and used as a seed to grow a new possible protein-complex core. Once a protein-complex core is completed, all vertices in it will be tagged with “1” and cannot be extended into any other protein-complex cores. The subroutine will stop when the queue Q is empty.Firstly, the always active proteins are chosen as candidates of core proteins by evaluating the expression values of each protein in the cell cycle. For an always active protein Q, we first look for its corresponding preliminary core. The preliminary core is composed of a seed vertex and its always active neighbors. Note that the neighbors that have been tagged with “1” will not be included. Since proteins in the protein-complex core have relatively more interactions between them, the protein-complex cores should be densely connected subgraphs. Here, a final protein-complex core is generated by removing vertices recursively from the preliminary core according to the edge clustering coefficient until its density is larger than or equal to a given threshold Td. For an edge  ∈ E, its edge clustering coefficient [u, v) is defined as the number of triangles to which  belongs, divided by the number of triangles that might potentially include , as shown in the following equation:Zu,v).Before generating the final protein-complex core, for a seed vertex picked from the queue fficient  ECC < Td, the edge clustering coefficient ECC of each edge  connecting the seed vertex v and a rest vertex u is calculated. Then, vertex u with the minimum ECC will be removed from the preliminary core C. This operation will be repeated until the density of the new preliminary core C is larger than or equal to Td. The preliminary core C with den(C) ≥ Td is outputted as a potential protein-complex core.For a preliminary core V which are not included in PA are considered as potential attachments. On a certain time course, an attachment protein can only participate in one protein complex.After finding the possible protein-complex cores, the key point is how to find the attachments for each core to form protein complexes. One of the most important features of Core-Attachment conception is that the attachment proteins generally bind to the core proteins to carry out cellular functions and are short-lived. Here, all the proteins in PR (PR = V − PA) expressed in a certain time course of the molecular cycle. We say that there is a connection between a protein-complex core and an attachment if there is at least an edge connecting the attachment and one protein in the protein-complex core. An attachment may have connections with multiple protein-complex cores. Here, for each attachment in the set PR (PR = V − PA) we select a best one with the highest value of “closeness” as its potential protein-complex core. The “closeness” of an attachment v to a core Ci is defined as follows:Based on this idea, we first find a best protein-complex core for each potential attachment in the set v tends to be involved in the protein-complex core with the largest CL. By computing the “closeness” of each attachment protein in PR to all the possible protein-complex cores, we can get a best corresponding one for each attachment. If the “closeness” of an attachment to all the possible protein-complex cores is equal to 0, this attachment will be ignored. In other words, such a protein in PR will not be an attachment of any possible protein-complex core.It is obvious that an attachment PR. For each time course in the molecular cycle, every protein in PR will be judged whether it is active or not. If a protein in PR is active in a certain time course, it will be considered as an attachment and added to its corresponding protein-complex core at this time course. Finally, the protein-complex cores and their attachments with the expressed time course will be outputted. The description of generating potential dynamic protein complexes can be seen in Then, the potential dynamic protein complexes will be generated based on these correspondences of possible protein-complex cores to attachments in In the second phase, all the possible dynamic protein complexes are identified by combining the protein-complex cores and their corresponding attachments. For a protein-complex core, only one possible protein complex will be kept if its corresponding attachment groups are the same at different time courses. A protein-complex core may have several different groups of attachments or have no attachments. In the last phase, we will reexamine the possible dynamic protein complexes to filter some false positives. For instance, a protein complex will not provide any information if it only consists of one protein and such protein complex should be removed. According to the formation and function of a protein complex, it should be active in two or more continual time courses. Furthermore, a protein-complex core expressed in all the molecular cycle can be regarded as a protein complex even if it has no attachments.Based on the above analysis, we use the following rules to filter false positive complexes. (1) A protein complex should include at least two proteins. (2) The attachment proteins should be active in the same time course or in different but adjacent time courses. (3) If the attachments of a possible protein complex do not satisfy the second rule and the protein-complex core involves at least two proteins, the core will be kept as a final protein complex.Tg and Td. The user can get different protein complexes by modifying the values of  Tg and Td. In this paper, the typical threshold values of  Tg and Td used in previous works [The description of algorithm DPC is shown in us works , 29, 31  Saccharomyces cerevisiae, which was downloaded from the DIP database [ Saccharomyces cerevisiae. Our algorithm DPC was applied on the above PPI network and gene expression profile, which generated 766 dynamic protein complexes made up of cores and attachments. In the following subsections, the performance of our algorithm DPC and six other algorithms  were coProtein-complex cores are the most important sections of protein complexes. Some attachments bind to the cores to carry out their cellular functions and every protein complex has a unique core. Getting a protein complex from a bad core is likely to generate a protein complex by randomly selecting proteins from a PPI network. Analysis of protein-complex cores is essential for the algorithms based on Core-Attachments assumption.K be a set of known protein complexes and Ki be a known protein complex included in K; that is, Ki ∈ K. Given a protein-complex core C, its CMR is defined as follows:C∩Ki| denotes the number of proteins of C included in one known proteins complex Ki. When a protein-complex core C is completely included in a known protein complex Ki, CMR(C) = 1.In our works, protein-complex cores, as the “hearts” of protein complexes, are detected by the integration of PPI network and gene expression profile. Our algorithms DPC generated 550 protein-complex cores in total. Intuitively, a good core should be completely included in known protein complexes. Here, we explore a new indicator, named Core Matching Rate (CMR), to show the performance of cores matched by known complexes. Suppose C) = 1. When CMR(C) ≥ 0.5 , the percentage of cores predicted by DPC is 60% which is 15% higher than that predicted by COACH and 32% higher than that predicted by Core-Attachment. From The known protein complexes were collected from the literature published in Nucleic Acids Research . There PC) matches a known complex (KC). The formula of OS is shown as follows:PC∩KC| denotes the number of proteins in the intersection of PC and KC. |PC| and |KC| represent the number of proteins in PC and the number of proteins in KC, respectively. Here, not protein complexes but predicted cores are considered. The known cores from Gavin et al. [To further evaluate the quality of protein-complex cores produced by our algorithm DPC, COACH, and Core-Attachment, we compared them with the cores from Gavin et al.  which wen et al.  and the From  Nucleic Acids Research [PC) and a known complex (KC) is equal to 0  = 0), it means that any proteins in PC do not match to proteins in KC. When OS = 1, a predicted complex (PC) perfectly matches to a known complex (KC). A predicted complex can be considered as a match to a known complex if their overlapping score is equal to or larger than a specific threshold which is generally set to 0.2 [To evaluate the effectiveness of our algorithm DPC for detecting protein complexes, we compare the predicted complexes with known protein complexes published inResearch . Here, wResearch , 27, 28 t to 0.2 , 23, 27.Sp) is the fraction of the predicted complexes that are matched by the known complexes, divided by the total number of the predicted clusters. Sensitivity (Sn) is the fraction of the known complexes that are matched by the predicted clusters among the known complexes. The formula of specificity and sensitivity can be shown as follows:TP (true positive) is the number of the identified complexes that can be matched by one or more known complexes with OS≥0.2, FP  is the number of the detected complexes which can always be matched by each known complex with OS < 0.2, and FN  denotes the number of the known complexes that cannot be matched by any predicted complex with OS ≥ 0.2. Finally, based on the definitions of specificity (Sp) and sensitivity (Sn), a comprehensive evaluation indicator F-score can be defined as follows:In addition, two important indicators, specificity and sensitivity, are widely used to evaluate clustering algorithms. Here, we also used them to evaluate our algorithm DPC and some other previous related works. Specificity (Pm), the specificity (Sp), sensitivity (Sn), and F-score of our algorithm DPC and six other previous competing algorithms CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment are shown in Pm) denote that the number of the predicted protein complexes perfectly matched with known complexes.The number of predicted complexes and perfected matches (Pm), specificity (Sp), sensitivity (Sn), and F-score. For the Core-Attachment based algorithm, COACH, it produces 894 protein complexes with 16 perfect matches. However, our algorithm DPC has 24 perfect matches with the produce of 766 protein complexes which are less than those of COACH. COACH and DPC have the same specificity, but the sensitivity and F-score are a little higher than our algorithm DPC. The specificity, sensitivity, and F-score are calculated based on the overlapping score threshold equal to 0.2. However, with the increase of the overlapping score threshold, the predicted complexes of DPC match better to the known complexes than that of COACH. For example, when OS > 0.5, the percentage of matched complexes of DPC is 9.27% and that of COACH is 9.06%. When OS > 0.8, the percentage of matched complexes of DPC is more than 3%, however, that of COACH is less than 2%.As shown in F-score of our algorithm DPC is much higher than that of Core-Attachment. The former is about 1.6 times the latter.For another algorithm Core-Attachment, it produces 1634 protein complexes which are twice more than that generated by our algorithm DPC. The specificity of Core-Attachment is 0.18, which is much lower than our algorithm DPC and other algorithms HC-PIN and COACH. The lower specificity of Core-Attachment indicates that Core-Attachment produces a lot false positives when generating such a large number of protein complexes. Moreover, the comprehensive evaluation indicator Tf, a topology-free measurement, and hF-measureTb, a topology-based measurement. We use both hF-measureTf and hF-measureTb to evaluate the predicted complexes of our algorithm DPC and those of six other previous competing algorithms: CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment. The comparison results are shown in To get insights on the shared, underlying molecular function of the identified protein complexes, we use Gene Ontology annotations, downloaded from the Saccharomyces Genome Database (SGD) , to analTf and hF-measureTb of our algorithm DPC are consistently higher than those of six other previous competing algorithms: CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment. The comparison results show that no matter whether the topologies are considered, the predicted complexes of DPC have good functional enrichments. In other words, the protein complexes produced by DPC have more chances to implement the same or similar functions and these proteins generally tend to have more interactions among themselves than others.As shown in Distinguished from other previous Core-Attachment based algorithms, our algorithm DPC identified protein complexes by integration of PPI network and gene expression profiles. The implied dynamic information was also contained within the identified protein complexes. The protein-complex cores are a group of proteins which are always active in the molecular cycle and the attachments surrounding the cores are not always active but active in one or multiple time courses. As shown in As shown in The integration of gene expression data not only provides implied dynamic information but also contributes to more accurate identification of protein complexes. In postgenomic era, one of the key topics in system biology is to recognize life activity with a cell by protein interactions and protein complexes. Many computational algorithms have been proposed to identify protein complexes from static protein-protein interaction data. In reality, proteins and interactions between proteins are dynamic in cellular life. Identifying dynamic protein complexes has become an essential and challenging task in the system biology. In this paper, a novel algorithm DPC has been proposed to identify dynamic protein complexes by integrating PPI network and gene expression data. The algorithm DPC has been developed based on the Core-Attachments assumption that the always active proteins involved in the core and some other not always active proteins bind to the core dynamically. Saccharomyces cerevisiae. The experimental results based on matching with known protein complexes and cores have shown that our algorithm DPC can predict more accurate cores than COACH and Core-Attachment. In addition, our algorithm DPC outperforms the previous algorithms CMC, SPICi, MCL, and HC-PIN on the number of perfect matches, specificity and sensitivity and F-score. Moreover, all the identified protein complexes of DPC, CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment have been validated on the functional enrichments. The latest GO based methods hF-measureTf and hF-measureTb are used. The experimental results have shown that the hF-measureTf and hF-measureTb of the predicted complexes identified by our algorithm DPC are consistently higher than that of six other previous competing algorithms: CMC, MCL, SPICi, HC-PIN, COACH, and Core-Attachment which indicates that no matter whether the topologies are considered, the predicted complexes of DPC have good functional enrichments. In this paper, only gene expression is integrated. Actually, more biological information, such as subcellular localization and biological processes, can also be integrated to help identify protein complexes more accurately. This will be one of our future works.The effectiveness of our algorithm DPC has been tested on the PPI network and gene expression of


Protein function prediction is to assign biological or biochemical functions to proteins, and it is a challenging computational problem characterized by several factors: (1) the number of function labels (annotations) is large; (2) a protein may be associated with multiple labels; (3) the function labels are structured in a hierarchy; and (4) the labels are incomplete. Current predictive models often assume that the labels of the labeled proteins are complete, i.e. no label is missing. But in real scenarios, we may be aware of only some hierarchical labels of a protein, and we may not know whether additional ones are actually present. The scenario of incomplete hierarchical labels, a challenging and practical problem, is seldom studied in protein function prediction.Predict protein functions using Incomplete hierarchical LabeLs (PILL in short). PILL takes into account the hierarchical and the flat taxonomy similarity between function labels, and defines a Combined Similarity (ComSim) to measure the correlation between labels. PILL estimates the missing labels for a protein based on ComSim and the known labels of the protein, and uses a regularization to exploit the interactions between proteins for function prediction. PILL is shown to outperform other related techniques in replenishing the missing labels and in predicting the functions of completely unlabeled proteins on publicly available PPI datasets annotated with MIPS Functional Catalogue and Gene Ontology labels.In this paper, we propose an algorithm to The empirical study shows that it is important to consider the incomplete annotation for protein function prediction. The proposed method (PILL) can serve as a valuable tool for protein function prediction using incomplete labels. The Matlab code of PILL is available upon request.The online version of this article (doi:10.1186/s12859-014-0430-y) contains supplementary material, which is available to authorized users. The increasing amount of proteomic data produced using high-throughput technology makes it crucial but challenging to develop computational models that can identify hypothetical functions of proteins. Such techniques have the potential to drive the biological validation and discovery of novel functions of proteins, and to save on the experimental cost. At the same time, functional annotations of proteins have been incorporated into several bioinformatics tools  classifier; the authors observed that utilizing the correlation between function labels can boost the prediction accuracy. Zhang and Dai  be the original label set, with yik=1 if protein i has the k-th function, and yik=0 if it’s unknown whether this protein has the k-th function or not. We assume the first l≤n proteins are partially labeled and the remaining n−l proteins are completely unlabeled. We set the normalized function correlation matrix as Given Cm, we can estimate the likelihood of a missing function label on the i-th (i≤l) partially labeled protein as follows: Based on the definition of yik=0 and the k-th function label has a large correlation with the already known functions of protein i, then it is likely that the k-th function is missing for this protein, yi and Cm estimated likelihoods of the missing labels (for entries corresponding to yik=0) on the i-th protein.If l partially labeled proteins as follows: Based on i-th protein, F= is the predictions on n proteins, n proteins, U is an n×n diagonal matrix with Uii=1 if i≤l, and Uii=0 otherwise.where et al. [Proteins with similar amino acid sequences are likely to share the same functions. Schwikowski et al.  observedet al.  that stapi, Wij is the weight of the interaction (similarity) between proteins i and j, and I is an n×n identity matrix. Our motivation to minimize Eq. (i and j are quite similar to one another (or Wij is large), then the margin between fi and fj should be small, otherwise there is a big loss; (ii) if protein i has missing labels and its interacting partners do have those labels, then we can leverage this information to assist the replenishing process of the missing labels for protein i; (iii) if protein i is completely unlabeled, its labels can be predicted using the labels of its partners. Alternative ways  to transfer labels among proteins have been suggested in the literature . -41. \docure , ,16, \docBased on Eq.  and Eq. λ>0 is a scaler parameter that balances the importance of the empirical loss and the smoothness loss.where t, and the entry index corresponding to ‘01’ (the ancestor function label of ‘01.01’) is s, if yit=1, then yis=1.We report the results on three PPI networks, namely CollingsPPI, KroganPPI, and ScPPI. We annotated proteins in these networks according to MIPS FunCat  and GeneThere are no off-the-shelf proteomic datasets that can be directly used to test the performance of the solution of the incomplete labels problem, although this problem is practical and common in real world scenarios. To address this issue, we assume the labels of the currently labeled proteins are complete and randomly mask some of the ground truth leaf functions of a protein; these masked functions are considered as missing for this protein.m as the number of missing functions of a protein. For example, if a protein has 10 functional labels, m=3 means that three functional labels are masked for this protein. If a protein does not have more than m labels, we do not mask all the available labels and ensure it has one function label. A small number of proteins in these networks doesn’t have any label; we keep these proteins in the network to retain the network’s structure, but do not test on them. We introduce Nm to represent how many labels are missing for all the proteins for a given setting of m.For representation, we use LinSim in Eq. . Other results on CollingsPPI, KroganPPI and ScPPI are reported in Tables S1-5 of the Additional file m, the experiments are repeated 20 times. In each run, the masked labels of a protein are randomly chosen from the leaf function labels of the same protein, and these masked labels are considered as missing for testing. If s is a non-leaf function label of a protein, whenever its descendant function labels are all missing (or masked), s turns to be a leaf function label and can be masked for this protein.In this section, we conduct experiments to study the performance of PILL on replenishing missing annotations of m), PILL outperforms ProDM 85.56% of the cases, outperforms ProWL 91.11% of the cases, ties with them 4.44% and 4.44% of the cases, and loses to them in 5.56% and 4.44% of the cases, respectively. PILL outperforms LkNN, TPRw and Naive in all configurations. Taking MacroF1 on CollingsPPI annotated with FunCat labels, for example, PILL on average is 23.30% better than ProDM, 4.27% better than ProWL, 147.33% better than LkNN, and 106.61% better than TPRw. These results corroborate the effectiveness of PILL on replenishing the missing labels.From the results reported in these Tables, we can observe that PILL outperforms other competitive methods across all the evaluation metrics in most cases. In summary, out of 90 configurations  to PILL in few cases.AvgROC and MacroF1 scores, a medium 1-RankLoss score, and almost the lowest Fmax and MicroF1 scores among the comparing methods. Naive performs better than some methods in few cases, but it is outperformed by PILL by a large margin across all the evaluation metrics. These results show that PILL can exploit the hierarchical and flat relationships among labels to boost the performance of protein function prediction.LkNN and TPRw explicitly utilize the hierarchical relationship among labels, but they are not able to compete with PILL, ProDM and ProWL. The cause is two fold: (i) LkNN and TPRw assume that the labels of the labeled proteins are complete, and they use partially labeled proteins to predict missing labels without estimating the missing labels in advance; (ii) they do not utilize the flat relationships among function labels. Naive ranks the functional labels according to their frequency and sets the frequency as the predicted probability for the labels. Since the missing labels are ‘leaf’ functional labels, and their frequencies are smaller than the ‘non-leaf’ functional labels, Naive achieves the lowest Real Life Examples: Another experiment is performed to study the ability of PILL on providing hypothetical missing labels. In particular, we use the GO terms associations (download date: 2014-02-01) of S. Cerevisiae to annotate the proteins in ScPPI . We use PILL to replenish the missing labels of these proteins. There are 117 proteins in ScPPI having new labels in the updated GO terms annotations [otations  of incomplete labels as in the previous experiments. The experimental results with respect to CollingsPPI are reported in Table We performed another set of experiments to test the performance of PILL on predicting functions for completely unlabeled proteins using partially labeled proteins. In these experiments, 1-RankLoss and Fmax, which are more favorable to the frequency based ranking than other metrics). However, Naive is outperformed by PILL by a large margin. Given the superior performance of PILL to Naive, PILL can serve as a valuable method for protein function annotation.From these tables, we can observe that PILL achieves the best results among all the comparing methods. PILL, ProDM and ProWL take into consideration the incomplete annotation in the training set, and they often outperform LkNN, TPRw, and CIA. MLR-GL considers the incomplete annotation in the training set, but it does not explicitly use the hierarchical relationship between labels. Thus, it loses to the competing algorithms. TPRw post-processes the predictions of binary classifiers according to the true path rule, and sometimes it achieves comparable results to PILL. For a fair comparison with the other algorithms, we do not apply the true path rule to refine the predictions made by PILL in Eq. . Naive, From these results, we can draw the conclusion that it is important to utilize the relationships  among labels, and to explicitly consider the incomplete label problem in protein function prediction. These results also corroborate the effectiveness of PILL on predicting protein functions on unlabeled proteins using hierarchical incomplete labeled proteins.ComSim in Eq.  PILL-Jcd is PILL with the function correlation defined by the Jaccard coefficient; (ii) PILL-Hsim is PILL with the function correlation defined by HSim using the shared ancestors in Eq. (LinSim using the common ancestors in Eq. (AvgROC and 1-RankLoss) on CollingsPPI annotated with FunCat labels are reported in Figure We did another kind of experiments to investigate the benefit of using the proposed m in Eq. . ComSim s in Eq. . (iii) Ps in Eq. ; From thLinSim, HSim (a variant of LinSim) or the Jaccard coefficient separately often cannot achieve results comparable to PILL. PILL-Hsim based on HSim uses the shared ancestor labels, and performs better than PILL-Lin based on LinSim, which utilizes the common ancestor labels. This fact supports our motivation to define the HSim using the shared ancestor labels instead of the common ones. The superiority of PILL over PILL-Jcd indicates that hierarchical relationships between function labels are more important than flat relationships. The larger the number of missing labels, the larger the performance margin between PILL and PILL-Jcd is. These observations support our motivation to use ComSim to exploit both the hierarchical and flat relationships between labels to boost the performance.From these figures, we can observe that using AvgROC and 1-RankLoss) on CollingsPPI with respect to FunCat labels are given in Figure We conducted experiments to study the benefit of using function correlations and the guilt by association rule. We define two variants of PILL: (i) PILL-FC just utilizes the estimated ) in Eq. , and (ii) in Eq.  and doesmentclass2pt{minimFrom these results, we can say that using the function correlation or the guilt by association rule separately cannot replenish the missing labels as well as PILL. PILL-FC often achieves better results than PILL-GbA. This fact shows that using function correlation alone can replenish the missing labels to some extent. From these results, we can draw the conclusion that both the function correlations and the guilt by association rule are beneficial to replenish the missing labels of proteins, and PILL can jointly utilize these two components to boost the performance of protein function prediction.In this article, we investigated the seldom studied  problem of protein function prediction with partial and hierarchical labels. We proposed an approach, PILL, to replenish the missing labels of partially labeled proteins and to predict functions for completely unlabeled proteins. Our empirical study shows that PILL outperforms a range of related methods and PILL can confidently provide hypothetical missing labels from a large number of candidate labels.Some methods have been proposed to explore node-based (or edge-based) similarities to measure the semantic similarity of functional labels ,43. Thes


Our knowledge of global protein-protein interaction (PPI) networks in complex organisms such as humans is hindered by technical limitations of current methods.On the basis of short co-occurring polypeptide regions, we developed a tool called MP-PIPE capable of predicting a global human PPI network within 3 months. With a recall of 23% at a precision of 82.1%, we predicted 172,132 putative PPIs. We demonstrate the usefulness of these predictions through a range of experiments.The speed and accuracy associated with MP-PIPE can make this a potential tool to study individual human PPI networks  for personalized medicine.The online version of this article (doi:10.1186/s12859-014-0383-1) contains supplementary material, which is available to authorized users. Protein-protein interactions (PPIs) are essential molecular interactions that define the biology of a cell, its development and responses to various stimuli. Physical interactions between proteins can form the basis for protein functions, communications, and regulation and controls within a cell. Such interactions can result in the formation of protein complexes that perform specific tasks. Similarly, internal and external signals are often realized and communicated through the formation of stable or transient PPIs. Due to their central importance to the integrity of communication networks within a cell, PPIs are thought to involve important targets for drug discovery  and are Saccharomyces cerevisiae )ractions . Given a over 7%  and 12.4 [et al.  requires [et al.  predicts [et al. , leaving [et al. , Zhang ein vitro binding assays [in vivo functional analysis [A subset of cellular PPIs is mediated by defined short, linear polypeptide sequences -18. Leveg assays , and in analysis . An addeanalysis . We noteS. cerevisiae [We have developed a computational tool termed the Protein Interaction Prediction Engine (PIPE) that uses co-occurrence of short polypeptide regions to detect novel PPIs in revisiae . AlthougS. cervisiae or C. elegans, is the complexity of the human proteome. More precisely, when predicting human protein interactions using previous methods [One of the main issues when predicting human protein interactions on a large scale, which does not occur for simpler organisms such as  methods , the com methods  was rank methods . HoweverIn the following, we discuss the runtime performance of MP-PIPE on different hardware architectures which eventually enabled us to perform global PPI analysis of a human cell within three months. More precisely, we tested our MP-PIPE solution on three different compute clusters. These clusters included a six node cluster with 24 total compute cores , a 32 node cluster with 128 total compute cores (medium cluster), and a 50 node cluster with 6,400 total hardware supported threads (large cluster). The performance of MP-PIPE was initially tested on a single large cluster node with varying numbers of threads, and then in a second test we increased the number of nodes  test of MP-PIPE using the 41,678 experimentally verified, high-confidence human PPIs taken from BioGrid and 100,000 randomly chosen negative protein pairs (assumed to not interact). Choosing negative data in this way avoids sources of bias introduced by other methods . TheH. sapiens proteome, the results in the above confusion matrix require adjustment. This adjustment to account for the true prevalence of PPIs among all protein pairs leads to a more conservative and realistic estimate of predictive performance of MP-PIPE. We have used a ratio of 100 non-interacting protein pairs per interacting pair. We feel this is a more realistic estimate given the expected sparsity of the actual interaction network and the range of estimates reported in previous studies [Since the ratio of known interacting pairs to assumed non-interacting pairs in our test set  is not representative of the true ratio expected among all protein pairs within the  studies ,27. The A wide variety of performance metrics are commonly used to assess PPI prediction methods. These are summarized and computed in Table This leave-one-out test was repeated with all homologs removed from our human dataset as in . This reet al. [After three months of 24/7 computation on the 50 fully dedicated nodes of the large cluster , MP-PIPE completed the scan of the human proteome. With the chosen operating point as described in the previous section, MP-PIPE predicted 172,132 protein interactions. Of these high confidence predictions, 132,710 protein interactions have never been reported previously. Given that 41,678 human protein interactions are known (previously reported) and were included in the MP-PIPE database and would therefore be predicted to interact, MP-PIPE has potentially more than quadrupled our knowledge of the human interaction network. At the chosen operating point, MP-PIPE data covers more than one fifth of the estimated human PPI landscape. In comparison, Elefsinioti et al.  and 12.4et al.  of the tBesides leave one out cross-validation, another standard method for evaluating PPIs is to check whether the proteins pairs predicted to interact are co-located within the same cellular component, have the same molecular function, are involved in the same biological process or have a common third party interacting partner. The results of this analysis are shown in Table In addition to the above cross-validation, we independently evaluated the competence of our predictions by evaluating experimental data gathered using the Lentivirus-delivered, Gateway-compatible affinity Tagging System coupled with Mass Spectrometry (LGTS-MS) approach that has been recently developed for the identification of PPIs in mammalian cell lines . The LGTAs expected, we recovered both the bait and several well-known interacting protein partners, such as the interaction between the tagged histone binding protein, RBBP4 (Q09028) and subunits of the core histone deacetylase complex HDAC1 Q13547) and RBBP7 (Q16576)), confirming the overall efficacy of the protein purification procedure employed in the identification of co-purifying interacting proteins  for a coverage of approximately 50%. Since a total of 172,132 interactions were predicted, on average there appears to be approximately 15 interactions for each protein found in the prediction list. As illustrated in Figure revisiae . Of inteIn a PPI network, the degree of interaction for a target protein is believed to be a good indicator for the biological importance of that protein within the system ,35. RemoCentrality measurements are often used to predict the possible involvement of a protein in disease etiology and progression. Some studies suggest that hubs are likely enriched for disease proteins, whereas others incline towards betweenness centrality as a better indicator -46. We tOur computationally predicted interactome represents a comprehensive all-to-all interaction network in humans. This network generates a wide range of testable hypotheses concerning biological processes, and informs our understanding of the overall architecture of cellular function. Here, we demonstrate the usefulness of this new predicted interactome through prediction of gene functions, experimental verifications and analysis of putative protein complexes.Breast cancer is the most commonly diagnosed form of cancer among women . BRCA1 aCDK3 is a cyclin-dependent kinase that functions in cell cycle progression and mitosis, and plays an essential role in G1/S transition through its activation of the E2F transcription factor family and G0/G1 transition by Rb phosphorylation . E2F andAURKB has several functions during mitosis, including spindle assembly, chromosome segregation, and cytokinesis . AURKB hSMC1B is a meiosis-specific protein involved in chromosome segregation during anaphase, synapsis, and recombination . SMC1B iWe also examined the PPI network for mutations associated with resistance to breast cancer therapeutics doxorubicin and Trastuzumab. Individual mutant proteins were analyzed against the human proteome  for their PPIs at a recall of 23% at a precision of 82.1%. In this way, 5 personalized human PPI networks were predicted, each differing by a mutation in one gene only. The 5 PPI profiles were compared to that of their corresponding control networks. The list of these mutants is found in the table in Additional file Glucocorticoids (GCs) have a key role in the treatment of patients with seasonal allergic rhinitis (SAR) and other allergic disorders . BecauseProteins from the acute phase response pathway, complement signaling pathway and glucocorticoids receptor pathway were extracted from the Ingenuity pathway analysis (IPA) software. Interactors of these proteins were selected from our predicted human PPI network. We included secreted, membrane and cytoplasmic proteins, but excluded nuclear proteins. We prioritized candidate biomarkers based on their number of known and predicted interactions with proteins known to be involved in SAR-associated responses. Next, we focused on proteins with a high number of predicted interactions.From the literature we extracted 191 proteins that belong to the acute phase response pathway, complement pathway, and glucocorticoids receptor pathway . PRB1 decreased from 16.3 ± 7.0 ug/mL to 8.5 ± 2.1 ug/mL  , Q08AM6 (VAC14), P23511 (NFYA), Q9UKR5 (ERG28) and P48735 (IDH2) with proteins known to play roles in the process of translation. To study the involvement of these five proteins in translation, we subjected their corresponding yeast homologs  to experimental analysis. First, we examined the effect of their deletion on stop-codon read-through using three different expression plasmids, pUKC817, pUKC818 and pUKC819 that carry premature stop codons UAA, UGA and UAG, respectively, within a hap2∆, erg28∆ and lys12∆ showed altered levels of sensitivity to streptomycin and cycloheximide . Our predictions are useful for understanding cellular biology as a whole, with approximately 8,000 protein complexes in our inferred interaction network. Furthermore, specific processes can be successfully interrogated using our new predictions: on the basis of inferred interactions we predict and experimentally confirm novel functions for proteins involved in translation, and identify new molecular markers for seasonal allergic rhinitis. Our analysis highlights the usefulness of the predicted PPIs for functional analysis of the human proteome. The speed associated with this approach sets the path for investigating the PPI map for individual humans in a timely fashion. Personal  PPI maps may improve our knowledge of network and personalized medicine.S. cerevisiae, C. elegans, or human), the PIPE algorithm relies on a database of known and experimentally verified protein interactions. For example, for the 22,513 human potential open reading frames included in the current study, only 41,678 high confidence interactions are known . Since experimental verification can have large numbers of false positives  of query proteins, our PIPE method predicts whether or not A and B interact.For a given organism (e.g. see e.g. ), the PIA is split up into overlapping fragments of size w. This can be thought of using a sliding window of size w across protein A. For each fragment ai of A, where 0 < = i < = |A| - w +1, we search for fragments "similar" to ai in every protein in graph G. A sliding window of size w is again used on each protein in G, and each of the resulting protein fragments is compared to ai. For each protein that contains a fragment similar to ai, all of that protein's neighbors in G are added to a list R. To determine whether two protein fragments are similar, a score is generated with the use of the PAM120 substitution matrix. If the similarity score is above a tuneable threshold then the fragments are said to be similar or to “match” (see pseudocode below). In the next step of the PIPE algorithm, protein B is split into overlapping fragments bj of size w (0 < = j < = |B| - w +1) and these fragment are compared to all (size w) fragments of all proteins in the list R produced in the previous step. We then create a result matrix H of size nxm, where n = |A| and m = |B| and initialize it to contain zeroes. For a given fragment ai of A, every time a protein fragment bj of B is similar to a fragment of a protein Y in R, the cell value at position  in the result matrix is incremented. The result matrix indicates how many times a pair  of fragments co-occurs in protein pairs that are known to interact. It is based on this matrix that the query proteins are predicted to interact or not. The following explains the basics of the algorithm in pseudocode:In the first step of the PIPE algorithm, protein A modified median filter, which simply sets a cell’s value to 1 if most of the neighbouring cells are greater than zero and zero otherwise, is applied and the two query proteins were predicted to interact if the average cell value was above a set threshold. By varying this threshold, a range of precision-recall values may be obtained  system is a massively parallel, high throughput protein-protein interaction prediction engine and is the first system that is capable of scanning the entire protein interaction network of complex organisms such as human. In order to achieve that goal, large numbers of concurrent PIPE instances need to be executed on a large-scale parallel compute cluster. This created two major challenges.S. cerevisiae and C. elegans, but lead to a large amount of unused resources when making predictions on more complex organisms such as human. Interestingly, the number of human proteins and protein pairs is not exceptional and simpler organisms such a C. elegans actually have more proteins and protein pairs than human. However, the human protein interaction network has more known interactions and a more complex structure. In particular, the calculation/prediction of these interactions is considerably more time consuming. Previous PIPE experiments for S. cerevisiae [C. elegans reported in [The first problem was the lack of scalability that made it difficult for large numbers of PIPE instances to effectively take advantage of all available computational resources without massive load imbalances. This load-balancing problem was not as significant in simpler organisms, such as revisiae ,22 and eorted in  showed tThe second major issue facing concurrent PIPE instances on a processor is inefficient usage of memory. Typically the number of PIPE processes running on a single machine is set to the number of compute cores on that machine. For example, on a quad-core machine there would typically be four PIPE processes running to utilize the chip fully. If different PIPE processes were left to work completely independently of each other, each process would have to load its own copy of the interaction graph along with all the other PIPE data. For less complex organisms this was not a major issue since the amount of data loaded was relatively small but the complexity of the human proteome translates into significantly more data needed by PIPE. The memory needs for a single PIPE instance for the human proteome increased to such a degree that running as many PIPE instances as compute cores can easily lead to program crashes due to a lack of memory. This would imply that processor cores would be left unused due to memory limitations. To process a global scan of all human protein pairs, this issue had to be overcome.The basic structure of MP-PIPE is a two-level master/slave and all-slaves model. A single MP-PIPE scheduler process is in charge of managing the main list of protein pairs to be processed as well as reporting the results. The MP-PIPE scheduler distributes work to several MP-PIPE worker processes in packets. Each packet contains a relatively small number of protein pairs. Each MP-PIPE worker executes the PIPE algorithm on protein pairs received from the MP-PIPE scheduler. By giving each worker only a relatively small amount of work at a time we ensure that if a worker does get stuck with abnormally time consuming protein pairs, the other workers will continue to work on their packets and, when they finish, they will request more work from the scheduler process and continue to work. This aspect of the MP-PIPE’s architecture deals with the load imbalance problem by ensuring that all PIPE processes are working as long as there is still work to be done. It should be noted however that if the packet size is too small then the amount of communication between the scheduler and worker processes will negatively impact the running time of the system. It is therefore important to balance the packet size between being too small (too much communication overhead) and too large .http://openmp.org/).To improve the memory efficiency, the second level of MP-PIPES’s architecture uses an “all-slaves” model. Each PIPE worker process consists of a number of parallel threads, called worker threads, among which it distributes the protein pairs to be processed. The worker threads of an MP-PIPE worker are to be executed on a shared memory multi-core processor. The PIPE interaction graph and other necessary PIPE data require considerable amounts of memory. For MP-PIPE, the data stored at an MP-PIPE worker process was re-designed to become a parallel data structure on which all worker threads for that worker can operate concurrently. Much care was taken to implement this as memory efficient as possible so that a single shared copy fits into the main memory of a processor node executing an MP-PIPE worker. This allowed more threads to run simultaneously on a given processor node by reducing the overall memory usage and solved the memory issues discussed. The scheduler/worker part of MP-PIPE was implemented using MPI (Message Passing Interface) and the worker threads within each MP-PIPE worker were implemented in OpenMP . The following represents the human proteome at the time that the MP-PIPE scan was performed.Total number of human proteins: 22,513Total number of protein pairs to examine: 253,406,328Total number of known protein interactions: 41,678Total number of proteins with at least one known interacting partner: 9,459Total number of proteins with no known interacting partners: 13,054Largest number of known interactions partners for a single protein: 265Average number of known interactions per protein: 3.70Average number of known interactions per protein with at least one interaction: 8.81MP-PIPE evaluated all possible protein pairs in the human proteome. Most of this work was performed on the large C. elegans proteome, and the average human protein has more than double the known interactions than a C. elegans protein. Coupled with the fact that the human proteins are, on average, longer than the C. elegans proteins, this significantly increases the complexity of scanning the entire human proteome. Furthermore, as outlined above, the running time for one individual protein pair can fluctuate between less than a second and more than 12 hours. This creates an additional load-balancing problem as discussed above. In fact, some individual protein pairs required six days of computation.The 22,513 human proteins used in this study are made up of the union of Uniprot “confirmed” proteins and proteins involved in physical interactions reported in BioGRID, with no isoforms removed. The 41,678 previously reported interactions were obtained from BioGRID, which is an amalgamation of several experimental studies. The human proteome has almost seven times more known interactions than the On the Victoria Falls cluster, 50 nodes were used, each with their own MP-PIPE worker process running 256 threads. This implies 12,800 parallel computational threads running on 6,400 hardware-supported threads. The number of threads per node was scaled down from 512 threads due to the fact that each individual thread needed significantly more memory than in our tests. The Victoria Falls cluster was used to process the vast majority of protein pairs. The results presented in this paper were obtained through three months of exclusive 24/7 use of the large Victoria Falls cluster. If one of its worker threads got stuck with a protein pair that was running more than 12 hours, that protein pair was off-loaded to the medium cluster since its individual cores are more powerful than a single Victoria Falls thread.v is the ratio of the number of shortest paths between a pair of nodes a and b on which v lies and the total number of shortest paths between a and b, summed over all possible pairs of nodes. These measures were used to identify proteins of interest. The betweenness centrality of a protein v is defined as σab(v) = # shortest paths from a to b through v, σab = # shortest paths between a and b.Protein interactions can be represented as an interaction network, where the proteins are interactors (nodes) and connections (interactions) are shown as edges. The number of edges incident to one node is called the degree of that node. Hubs are high degree nodes that interact with many other proteins (nodes) through various pathways . To find-verified clones were cloned into the lentiviral expression vector essentially as previously described [-confidence matches of the resulting MS/MS spectra was mapped to the reference human protein sequences using the SEQUEST database search engine with match quality evaluated using the STATQUEST algorithm [The full length, sequence verified, non-mutated, Gateway-compatible cDNA entry clones for the human chromatin-related proteins  were obtained from Harvard PlasmidID. The HEK293 and HEK293T cells were cultured in Dulbecco’s modified Eagle’s medium with 10% fetal bovine serum and antibiotics essentially as previously described ,80. The escribed ,80. The escribed ,80. The escribed ,80. The lgorithm . The ideThe comparison of LGTS-MS results with the MP-PIPE predicted and previously reported (known) interactions was done by calculating the precision and recall on the basis of LGTS-MS results representing real interactions. To do this we filtered the MP-PIPE predictions and known interactions to contain detectable proteins . Reachable proteins were defined as those proteins that interact directly with the bait or indirectly through one or two intermediaries within the sets of filtered interactions.Standard rich (YPD) and synthetic complete (SC) media were used as a growth media . To inve−2 to 10−5 cells/20 μl. From each dilution, 20 μl was spotted onto solid media containing translation inhibitory drugs. Media with no antibiotics was used as a control. All yeast cells were incubated at 30°C for 1–2 days [Yeast cells were grown in liquid YPD or SC media to mid-log phase (48 hours) and diluted to a concentration of 101–2 days .β -galactosidase expression cassette [β-galactosidase assay was performed using ONPG, O-nitrophenil-α-D-galactopyranoside, as descibed by [Translation fidelity was measured using plasmids pUKC817, pUKC818 and pUKC819, which carry premature stop codons UAA, UGA and UAG in a cassette ,83. Trancassette ,84. β-gacibed by .2O, 2.4 μl 10X PCR buffer, 1,25 μl dNTPs(4 mM), 1.25 μl MgCl2, 2 μl SYBR Green (1/4000), 7.6 μl primer mix (1 mM) and 3 μl Taq was used in addition to 5 μl template (cDNA). Thermocycler conditions were set to the following: 50°C for two min, 95°C for 10 min, 40 cycles of 95°C for 30s-60°C for 30s-72°C for 30s and a final 72°C for ten min [Total RNA was extracted using RNeasy Mini Kit (QIAGEN) according to manufacturer's instruction. To synthesize cDNA, 33 μl of RNA in combination with 3 μl poly T primer (random hexamer) was incubated for five min at 70°C, and then cooled on ice for five min. 6 μl RT-buffer, 15 μl dNTPs, and 3 μl RNaseI were added to the mixture and incubated for five min at 37°C. 3 μl RT enzyme was added to the mixture and incubated for an additional 1 hour at 42°C followed by aa 10 min incubation at 70°C. qPCR amplification and detection was performed on a Rotor Gene 3000 from Corbett Research. A final mixture of 2.4 μl H ten min ,88. The  ten min .P < 0.0000001). The study was approved by the Ethics Committee of the Medical Faculty of the University of Gothenburg. Written informed consent and questionnaire data sheets were obtained from all patients.40 patients with SAR were included in the study. SAR and symptom scores were defined as previously described ,90. TheiProteins from the acute phase response pathway, complement signaling pathway, and glucocorticoids receptor pathway were extracted from the Ingenuity pathway analysis (IPA) software. Interactors to these proteins were predicted using PIPE. We included secreted, membrane and cytoplasmic proteins, but excluded nuclear proteins. We prioritized candidate biomarkers based on their number of known and predicted interactions. Next, we focused on proteins with a high number of predicted interactions.Proteins were examined by ELISA in nasal fluids from 40 patients with SAR before and after GC treatment. V-akt murine thymoma viral oncogene homolog 2 (Akt2) was with an ELISA kit from R&D Systems Inc. . Proline-rich protein BstNI subfamily 1 (PRB1), proline-rich protein BstNI subfamily 2 (PRB2), v-yes-1 Yamaguchi sarcoma viral related oncogene homolog (LYN) and stratifin (SFN) were analyzed with ELISA kits from Uscnlife Life Sciences and Technology . All experiments were performed according to the manufacturer’s protocol. The Wilcoxon matched pairs signed ranks test was performed to compare two paired groups. A P-value less than 0.05 was considered significant.To decompose the predicted protein pairs into putative complexes, we applied a novel algorithm that combines pre-existing graph-theoretic tools with hierarchical clustering concepts. The algorithm has three independent stages: the initialization stage, which consists of generating an initial set of clusters, the merge stage, which determines which two clusters to merge next, if any, and the glom stage, which evaluates vertices for inclusion into a cluster. The initialization stage is run once, after which the merge and glom stages run alternately until either the desired number of clusters is reached or until neither stage results in a change to any cluster.Since initialization is an independent step, any initial clustering may be used. It is not required that the initial clustering be overlapping, although stages two and three may grow the clusters so that the end result is overlapping. We chose to use the set of all maximal cliques as the initial clustering. A clique is a set of vertices with all edges present; a clique is maximal if no vertex can be added to it to form a larger clique. The set of maximal cliques forms a natural overlapping clustering of a graph with the most rigid requirements, namely that all edges be present within each cluster. Real-world graphs often have many small and medium sized maximal cliques, and the protein prediction graph is no exception. These clusters are then allowed to merge and grow in stages two and three, gradually relaxing the stringency until the desired number of clusters is reached. To enumerate all maximal cliques, we used the well-known algorithm of Bron and Kerbosch described in  with bitm, then no clusters are merged.In the merge stage, the overlap of all clusters is evaluated and the two clusters with the highest overlap proportion are merged. If no two clusters overlap by a proportion greater than a parameter g, the glom factor, are added to the cluster. The first time through the glom stage, every cluster is considered. Subsequent glom stages only consider the cluster newly created by the merge stage, as all other clusters having already been considered.In the glom stage, every vertex not already belonging to a particular cluster is considered for inclusion into a cluster in similar fashion to the paraclique algorithm described in . Those va, the approximation parameter. For the protein prediction graph, which was initialized with more than 100,000 maximal cliques, we ran the approximation version until the number of clusters reached 20,000, at which point we switched to the exact version. Ultimately, a list of 8,739 paracliques were identified and characterized through a statistical analysis of the GO annotations of each member protein.In practice, calculating all pairwise overlaps to find the highest degree of overlap can make the merge stage computationally prohibitive. A small change, however, yields a good approximation version that can be run until the number of clusters is reduced to the point where the exact version can take over. Rather than merging the clusters with the highest overlap, the approximation version merges the first two clusters encountered with overlap at least


All cells are subject to ever-changing environments to which they have to adapt, using their sensory system to provide input for the regulatory systems that integrate the information and trigger the eventual effectors. These cascades constitute a very complex cellular wiring that is highly relevant due to its medical importance. The omni-present application of high-throughput analysis techniques has resulted in an unprecedented level of available detail about gene expression and various aspects of cellular proteins, such as abundance, function and localization, often captured in well-curated compendia that are publicly available.Although these information-rich inventories exist, the adaptive nature of protein complexes and signalling cascades remain poorly understood, as the current predominant approaches are not always suited to describe the associations between proteins. For example, binary protein interactions do not necessarily occur in vivo as the proteins could be expressed in different compartments of the cell or at different time points. This severely complicates the analysis of any protein interaction data. It thus remains a challenge to find out how biological entities cooperate to regulate cellular response to stimuli.Homo sapiens that report differentially expressed proteins in cell lines. Next, we analysed this collection with frequent itemset mining to identify proteins that are often co-occurring in publications and used these patterns as the backbone structure of our further analysis. These patterns of co-occurring proteins were enriched with additional attributes, such as gene expression correlation, protein localization and functional coherence metrics derived from the Gene Ontology tree [We used an integrative method, reliant on advanced pattern mining approaches to gain a deeper understanding of protein network dynamics. To this end, we created a compendium consisting of a large amount of proteomics papers for ogy tree  and usedWe found that several proteins and GO-functions, such as transcriptional regulation, are consistently reported and deemed significant regardless of the research topic. Furthermore, we were able to find associations across the various "omics" levels that are conserved in a wide range of human cancers and managed to identify lists of frequently occuring patterns that can be used to classify between pre- and post-metastasic tumour development.Pattern-based analysis on multiple "omics" levels can be used to identify the cellular logic circuits and holds many promising applications in the biotechnological and biomedical areas.


Malaria is a major health threat, affecting over 40% of the world's population. The latest report released by the World Health Organization estimated about 207 million cases of malaria infection, and about 627,000 deaths in 2012 alone. During the past decade, new therapeutic targets have been identified and are at various stages of characterization, thanks to the emerging omics-based technologies. However, the mechanism of malaria pathogenesis remains largely unknown. In this paper, we employ a novel neighborhood subnetwork alignment approach to identify network components that are potentially involved in pathogenesis.P. falciparum, using the protein-protein interaction networks in Escherichia coli as references. Eighteen out of these 24 proteins are associated with 418 other proteins that are related to DNA replication, transcriptional regulation, translation, signaling, metabolism, cell cycle regulation, as well as cytoadherence and entry to the host.Our module-based subnetwork alignment approach identified 24 functional homologs of pathogenesis-related proteins in the malaria parasite The subnetwork alignments and subsequent protein-protein association network mining predicted a group of malarial proteins that may be involved in parasite development and parasite-host interaction, opening a new systems-level view of parasite pathogenesis and virulence. Malaria is a severe global infectious disease. The World Health Organization estimated 207 million clinical cases, and 627,000 deaths due to malaria in 2012. Malaria infection was reported in 97 countries in 2013, representing a major public health concern. The vast majority of the deaths occurred in children under age 5 in sub-Saharan areas; it is estimated that a child dies from malaria every minute.Plasmodium. Among five human malaria parasites, Plasmodium falciparum causes the most severe form of malaria. It is notoriously difficult to study malaria biology as the parasite has a complex life cycle involving a mosquito vector and a human host. Within a human host, the parasite has a relatively dormant liver stage and an active red blood cell (RBC) stage. Clinical symptoms such as high fever, chills, headache, profuse sweats, fatigue, nausea, and vomiting are manifested at the blood stage, the stage that most drugs target against.The causative agents of malaria are a group of parasites from the Genus The effectiveness of antimalarial drugs, however, has been constantly challenged during the past decades due to the fast evolution of parasites that are resistant to multiple lines of drugs. This underscores an urgent need to search for novel drug targets. New lines of antimalarial targets have been identified -5 and arP. falciparum parasites from different continents were due to "overlapping, but not identical, sets of genes, including many encoding unknown proteins" [By nature, pathogenicity is an emergent property, that is, it is the result of the concerted effects of many genes. The suggestion by Mu et al. that differences in drug resistance in roteins"  and the roteins"  are bothroteins" . HoweverP. falciparum, we developed a module-based subnetwork alignment approach. Traditional homology transfer using a protein-protein network alignment approach [P. falciparum and other known model organisms: BLAST-based sequence similarity search was unable to make functional annotation for over 60% of the genes in P. falciparum [P. falciparum [To unveil previously unknown proteins that are potentially involved in pathogenesis in approach -54 does lciparum . Our neilciparum  could polciparum ,56. In tP. falciparum genes  family. PfEMP1 is encoded by the highly polymorphic Var gene, and is one of the largest and most widely-studied protein families in P. falciparum. PfEMP1 is believed to play an important role in parasite pathogenesis and immune evasion [Malaria pathogenesis is an emergent property that involves dynamic regulation of parasite metabolism, parasite signaling, and host-parasite interactions . Gene On evasion ; it cont evasion .P. falciparum were functional orthologs of proteins known to be involved in pathogenesis in E. coli . The network centralization parameter, a measure of the distribution of network density , is 0.05P. falciparum proteins. They are active players in crucial cellular processes in parasite development, growth, and response to host and environmental stresses. In the following sections, we discuss critical network components and their potential associations with pathogenesis and virulence.Table 1. Tight associations between translation, protein turnover, proteolysis, signaling, and stress responseP. falciparum, which includes small and large subunit ribosomal proteins of varying sizes. This complex is associated with several translation initiation and elongation factors. On the other hand, L40/UBI is implicated in the ubiquitin-proteasome system (UPS) [P. falciparum for degrading misfolded or unneeded proteins [P. falciparum receptor for activated C kinase homolog, PF3D7_0826700) are associated with a high confidence score (0.908), suggested by the co-expression of their homologs in Caenorhabditis elegans, Drosophila melanogaster, and humans, and protein-protein interaction found by affinity capture-MS assay in humans. The evidence that PfRACK could directly inhibit inositol 1,4,5-trisphosphate receptor-mediated calcium-signaling in mammalian host cells is indicative of a pathogenesis mechanism acting through the disruption of host activities [The predicted functional ortholog with the highest connectivity 180) is a putative 60S ribosomal protein L40/UBI PF3D7_1365900)  . UPS is proteins . UPS is proteins . Notablytivities . (2) Gen900  is a putative DEAD/DEAH box ATP-dependent RNA helicase PF3D7_1241800)  and GEXP11 (PF3D7_0731100)  from E. coli pathogenesis-related proteins and all P. falciparum proteins, the E. coli subnetworks were combinatorically aligned against the P. falciparum subnetworks. When good alignments were found (see below), the central protein of the best-aligned P. falciparum subnetwork was labeled a functional ortholog of the cognate protein at the center of the relevant pathogenesis subnetwork in E. coli.After deducing the neighborhood subnetworks for both the query P. falciparum and E. coli neighborhood subnetworks a shortest-path graph kernel was used to measure the similarity between two labeled networks, and a numerical score for each alignment was assigned [p Swith central protein p and a query subnetwork q Swith central protein q, the shortest path similarity function is defined as follows,In order to analyze and score the degree of similarity between the assigned . To optiwheredist is the length of the shortest path connecting proteins × and y in the PPI subnetwork. The computation was done on a -log 10 scale. The method outlined here takes each pair of proteins  from one subnetwork and seeks the maximum ratio of sequence similarity with respect to the closeness  of the networks, in order to identify proteins  in the target subnetwork. Using this algorithm, a subnetwork alignment score is obtained by collecting the shortest paths between two neighborhood subnetworks, getting an alignment score for each pair of proteins, and totaling all of the alignment values. Hence, by quantifying the sequence similarity and network similarity and evaluating the role of the central protein in the query network we can summarize the functional coherence, and distance between two central proteins, as a numerical score.E. coli from the IntAct database [P. falciparum from the STRING database [We downloaded protein-protein interaction data for database , and prodatabase . STRING database . EggNOG database . Networkdatabase .GO: Gene OntologyHAT: histone acetyltransferaseIPK: inositol polyphosphate kinasePCNA: proliferating cell nuclear antigenPlasmodium falciparum erythrocyte membrane proteinPfEMP1: P. falciparum receptor for activated C kinasePfRACK: PPI: protein-protein interactionRBC: red blood cellRRF: ribosome-recycling factorSUMO: small ubiquitin-related modifierUPS: ubiquitin-proteasome systemThe authors declare that they have no competing interests.YW and RK conceived and designed the study. All authors performed bioinformatics data analysis and drafted the manuscript. All authors read and approved the final manuscript.P. falciparum proteins that are annotated to be associate with Gene Ontology (GO) term GO0009405 (pathogenesis).Click here for fileP. falciparum. The query genome is P. falciparum, and the target genome is E. coli. GO: Gene Ontology. BP: Biological Process. MF: Molecular Function. CC: Cellular Component.Predicted functional orthologs involved in pathogenesis in Click here for file