Protein Complex Detection in PPI Network by Identifying Mutually Exclusive Protein-protein Interactions

A method for identifying protein complexes with the features of joint co-localization and joint co-expression in static PPI networks

The protein-protein interaction (PPI) networks can be regarded as powerful platforms to elucidate the principle and mechanism of cellular organization. Uncovering protein complexes from PPI networks will lead to a better understanding of the science of biological function in cellular systems. In recent decades, numerous computational algorithms have been developed to identify protein complexes. However, the majority of them primarily concern the topological structure of PPI networks and lack of the consideration for the native organized structure among protein complexes. The PPI networks generated by high-throughput technology include a fraction of false protein interactions which make it difficult to identify protein complexes efficiently. To tackle these challenges, we propose a novel semi-supervised protein complex detection model based on non-negative matrix tri-factorization, which not only considers topological structure of a PPI network but also makes full use of available high quality known protein pairs with must-link constraints. We propose non-overlapping (NSSNMTF) and overlapping (OSSNMTF) protein complex detection algorithms to identify the significant protein complexes with clear module structures from PPI networks. In addition, the proposed two protein complex detection algorithms outperform a diverse range of state-of-the-art protein complex identification algorithms on both synthetic networks and human related PPI networks.

Protein complexes play significant roles in cellular processes. Identifying protein complexes from protein-protein interaction (PPI) networks is an effective strategy to understand biological processes and cellular functions. A number of methods have recently been proposed to detect protein complexes. However, most of methods predict protein complexes from static PPI networks, and usually overlook the inherent dynamics and topological properties of protein complexes. In this paper, we proposed a novel method, called NABCAM (Neighbor Affinity-Based Core-Attachment Method), to identify protein complexes from dynamic PPI networks. Firstly, the centrality score of every protein is calculated. The proteins with the highest centrality scores are regarded as the seed proteins. Secondly, the seed proteins are expanded to complex cores by calculating the similarity values between the seed proteins and their neighboring proteins. Thirdly, the attachments are appended to their corresponding protein complex cores by comparing the affinity among neighbors inside the core, against that outside the core. Finally, filtering processes are carried out to obtain the final clustering result. The result in the DIP database shows that the NABCAM algorithm can predict protein complexes effectively in comparison with other state-of-the-art methods. Moreover, many protein complexes predicted by our method are biologically significant.

Protein–protein interaction (PPI) data derived from biological experiments include many false-positive interactions which are treated equally as other real physical interactions, thereby complicating the detection of real protein complexes from protein–protein interaction (PPI) networks. In this paper, a new weighting method, named as cwMINE (combined weight of module identification in networks), for detecting protein complexes efficiently in protein interaction networks is presented. cwMINE has a good combination between network topology and biological feature, which can solve false positives efficiently of PPI networks and make discovered protein complexes higher quality. In addition, a new expanding rule during the detection process, namely, expanding coefficient, is developed to filter edges with lower weights. The proposed method is compared with several state- of-the-art algorithms in three yeast PPI networks with two benchmark data sets. The experimental results show that the proposed method outperforms the other algorithms in most datasets in terms of the evaluation metrics. We further validate the effectiveness of our method on a human PPI network constructed from the HPRD dataset to identify important disease-related functional modules and provided valuable indications for disease treatment.

BackgroundAccurate determination of protein complexes has become a key task of system biology for revealing cellular organization and function. Up to now, the protein complex prediction methods are mostly focused on static protein protein interaction (PPI) networks. However, cellular systems are highly dynamic and responsive to cues from the environment. The shift from static PPI networks to dynamic PPI networks is essential to accurately predict protein complex.ResultsThe gene expression data contains crucial dynamic information of proteins and PPIs, along with high-throughput experimental PPI data, are valuable for protein complex prediction. Firstly, we exploit gene expression data to calculate the active time point and the active probability of each protein and PPI. The dynamic active information is integrated into high-throughput PPI data to construct dynamic PPI networks. Secondly, a novel method for predicting protein complexes from the dynamic PPI networks is proposed based on core-attachment structural feature. Our method can effectively exploit not only the dynamic active information but also the topology structure information based on the dynamic PPI networks.ConclusionsWe construct four dynamic PPI networks, and accurately predict many well-characterized protein complexes. The experimental results show that (i) the dynamic active information significantly improves the performance of protein complex prediction; (ii) our method can effectively make good use of both the dynamic active information and the topology structure information of dynamic PPI networks to achieve state-of-the-art protein complex prediction capabilities.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1101-y) contains supplementary material, which is available to authorized users.

Detecting protein complexes is one of the keys to understanding cellular organization and processes principles. With high-throughput experiments and computing science development, it has become possible to detect protein complexes by computational methods. However, most computational methods are based on either unsupervised learning or supervised learning. Unsupervised learning-based methods do not need training datasets, but they can only detect one or several topological protein complexes. Supervised learning-based methods can detect protein complexes with different topological structures. However, they are usually based on a type of training model, and the generalization of a single model is poor. Therefore, we propose an Ensemble Learning Framework for Detecting Protein Complexes (ELF-DPC) within protein-protein interaction (PPI) networks to address these challenges. The ELF-DPC first constructs the weighted PPI network by combining topological and biological information. Second, it mines protein complex cores using the protein complex core mining strategy we designed. Third, it obtains an ensemble learning model by integrating structural modularity and a trained voting regressor model. Finally, it extends the protein complex cores and forms protein complexes by a graph heuristic search strategy. The experimental results demonstrate that ELF-DPC performs better than the twelve state-of-the-art approaches. Moreover, functional enrichment analysis illustrated that ELF-DPC could detect biologically meaningful protein complexes. The code/dataset is available for free download from https://github.com/RongquanWang/ELF-DPC.

Biological functions of a cell are typically carried out through protein complexes. The detection of protein complexes is therefore of great significance for understanding the cellular organizations and protein functions. In the past decades, many computational methods have been proposed to detect protein complexes. However, most of the existing methods just search the local topological information to mine dense subgraphs as protein complexes, ignoring the global topological information. To tackle this issue, we propose the DPCMNE method to detect protein complexes via multi-level network embedding. It can preserve both the local and global topological information of biological networks. First, DPCMNE employs a hierarchical compressing strategy to recursively compress the input protein-protein interaction (PPI) network into multi-level smaller PPI networks. Then, a network embedding method is applied on these smaller PPI networks to learn protein embeddings of different levels of granularity. The embeddings learned from all the compressed PPI networks are concatenated to represent the final protein embeddings of the original input PPI network. Finally, a core-attachment based strategy is adopted to detect protein complexes in the weighted PPI network constructed by the pairwise similarity of protein embeddings. To assess the efficiency of our proposed method, DPCMNE is compared with other eight clustering algorithms on two yeast datasets. The experimental results show that the performance of DPCMNE outperforms those state-of-the-art complex detection methods in terms of F1 and F1+Acc. Furthermore, the results of functional enrichment analysis indicate that protein complexes detected by DPCMNE are more biologically significant in terms of P-score.

Detecting protein complexes from available protein-protein interaction (PPI) data will help to deeply understand the mechanism of the biological activities. In recent years, various computational methods have been developed for identifying protein complexes from PPI networks. Almost all the basic computational methods mainly depend on the association of topological analysis of PPI networks. However, most of them fail to satisfactorily capture the global and local topological structures of the PPI networks, as well as the diversity of connectivity patterns between individual nodes at the same time. To solve this problem, in this work we propose a node embedding based alias sampling extension method to detect protein complexes. More specifically, for a given set of seed nodes, it first uses the alias sampling strategy based on protein node embedding similarities to select potential addable nodes. Then it makes use of a new conductance measure, which could better quantify the likelihood of a subgraph being a protein complex, to decide whether to extend the current candidate subgraph in order to find protein complexes. Evaluated on six real yeast PPI networks, our method outperforms state-of-the-art methods in detecting protein complexes. Furthermore, the experimental results demonstrate the protein complexes predicted by our method have higher biological significance.

Protein complexes are aggregates of protein molecules that play important roles in biological processes. Detecting protein complexes from protein-protein interaction (PPI) networks is one of the most challenging problems in computational biology, and many computational methods have been developed to solve this problem. Generally, these methods yield high false positive rates. In this article, a semantic similarity measure between proteins, based on Gene Ontology (GO) structure, is applied to weigh PPI networks. Consequently, one of the well-known methods, COACH, has been improved to be compatible with weighted PPI networks for protein complex detection. The new method, WCOACH, is compared to the COACH, ClusterOne, IPCA, CORE, OH-PIN, HC-PIN and MCODE methods on several PPI networks such as DIP, Krogan, Gavin 2002 and MIPS. WCOACH can be applied as a fast and high-performance algorithm to predict protein complexes in weighted PPI networks. All data and programs are freely available at http://bioinformatics.aut.ac.ir/wcoach.

Detection of complexes, or groups of functionally related proteins, is an important challenge while analysing biological networks. However, existing algorithms to identify protein complexes are insufficient when applied to dense networks of experimentally derived interaction data. Therefore, we introduced a graph clustering method based on Markov clustering algorithm to identify protein complex within highly interconnected protein-protein interaction networks. Protein-protein interaction network was first constructed to develop geometrical network, the network was then partitioned using Markov clustering to detect protein complexes. The interest of the proposed method was illustrated by its application to Human Proteins associated to type II diabetes mellitus. Flow simulation of MCL algorithm was initially performed and topological properties of the resultant network were analysed for detection of the protein complex. The results indicated the proposed method successfully detect an overall of 34 complexes with 11 complexes consisting of overlapping modules and 20 non-overlapping modules. The major complex consisted of 102 proteins and 521 interactions with cluster modularity and density of 0.745 and 0.101 respectively. The comparison analysis revealed MCL out perform AP, MCODE and SCPS algorithms with high clustering coefficient (0.751) network density and modularity index (0.630). This demonstrated MCL was the most reliable and efficient graph clustering algorithm for detection of protein complexes from PPI networks.

Identifying overlapping protein complexes in protein-protein interaction (PPI) networks can provide insight into cellular functional organization and thus elucidate underlying cellular mechanisms. Recently, various algorithms for protein complexes detection have been developed for PPI networks. However, majority of algorithms primarily depend on network topological feature and/or gene expression profile, failing to consider the inherent biological meanings between protein pairs. In this paper, we propose a novel method to detect protein complexes using pseudo-clique extension based on fuzzy relation (PCE-FR). Our algorithm operates in three stages: it first forms the nonoverlapping protein substructure based on fuzzy relation and then expands each substructure by adding neighbor proteins to maximize the cohesive score. Finally, highly overlapped candidate protein complexes are merged to form the final protein complex set. Particularly, our algorithm employs the biological significance hidden in protein pairs to construct edge weight for protein interaction networks. The experiment results show that our method can not only outperform classical algorithms such as CFinder, ClusterONE, CMC, RRW, HC-PIN, and ProRank +, but also achieve ideal overall performance in most of the yeast PPI datasets in terms of composite score consisting of precision, accuracy, and separation. We further apply our method to a human PPI network from the HPRD dataset and demonstrate it is very effective in detecting protein complexes compared to other algorithms.

Protein complex detection is an important issue in the field of system biology, which is crucial to understanding the cellular organization and inferring protein functions. In recent years, various computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) networks. Unfortunately, most of these methods only use the local information of PPI networks and treat protein complexes as dense subgraphs, ignoring the global topology information of PPI networks. To address these limitations, we propose a new method, named OPCMTS, to detect overlapping protein complexes by simultaneously considering the local topological information and global topological information of PPI network. First, a local similarity matrix is constructed via calculating the Jaccard coefficients between proteins in the original PPI network. Then, we adopt a hierarchical compressing strategy to get multiple levels of gradually compressed smaller networks from the original PPI network and apply a network embedding model to learn protein embeddings from the compressed networks at multiple levels. The protein embeddings from these networks are concatenated and a dimensionality reduction strategy is adopted to remove the redundancy of the concatenated embeddings to generate the final embeddings. Further, a global similarity matrix is constructed by calculating the cosine similarity of the final embeddings. Finally, a core-attachment strategy is used to detect overlapping protein complexes based on the local and global similarity matrices. The experimental results prove that the proposed OPCMTS method outperforms other five state-of-the-art methods in terms of F-measure on two yeast datasets.

Protein interactions are an important layer of connectivity between cell components and cell processes. They allow the formation of protein complexes and mediate post-translational protein modifications. Disruption of protein-protein interactions may result in disruption of the cell component or process to which they contribute, compromising the cell viability or even leading to cell death. The study of protein interactions and their function has recently jumped to an organism scale, following the development of high-throughput yeast-two-hybrid screens mapping protein-protein interactions (–7) and affinity purification followed by mass spectrometry (MS) detecting protein complexes (8,9) (see Chapters 5, 6, and 7 for a thorough discussion of mass spectrometry and MS-based tissue imaging).At the cellular level, protein interactions are represented by the protein interaction network, which is the union of all proteins and the interactions among them. The protein interaction network involves asking questions similar to those asked for specific protein interactions: how to map these interactions, which properties characterize them, how are these proteins connected to cell processes and functions, and what is the evolution of the protein network? These questions are now investigated at different levels, from specific protein-protein interactions, passing through protein complexes, signaling cascades, and other protein interaction network motifs, ending in the whole protein interaction network.The role of the protein interaction network is beyond the network itself and it materializes through its interaction with other cellular networks. For example, the cell response to a change in environmental conditions requires the coordination between the signaling (a subnet of the protein interaction network), and the gene regulatory and metabolic networks. Consequently the study of protein interaction networks should be put in the context of the cell function. It is only in this way that the protein interaction network description can be transformed into better understanding and prediction of novel features and behavior. In this review I cover these different aspects of protein interaction networks. It is not intended to be an exhaustive review but is complemented by cited literature, including reviews of more specific topics.

Identifying protein complexes in protein-protein interaction (ppi) networks is often handled as a community detection problem, with algorithms generally relying exclusively on the network topology for discovering a solution. The advancement of experimental techniques on ppi has motivated the generation of many Gene Ontology (go) databases. Incorporating the functionality extracted from go with the topological properties from the underlying ppi network yield a novel approach to identify protein complexes. Additionally, most of the existing algorithms use global measures that operate on the entire network to identify communities. The result of using global metrics are large communities that are often not correlated with the functionality of the proteins. Moreover, ppi network analysis shows that most of the biological functions possibly lie between local neighbours in ppi networks, which are not identifiable with global metrics. In this paper, we propose a local community detection algorithm, (lcda-go), that uniquely exploits information of functionality from go combined with the network topology. lcda-go identifies the community of each protein based on the topological and functional knowledge acquired solely from the local neighbour proteins within the ppi network. Experimental results using the Krogan dataset demonstrate that our algorithm outperforms in most cases state-of-the-art approaches in assessment based on Precision, Sensitivity, and particularly Composite Score. We also deployed lcda, the local-topology based precursor of lcda-go, to compare with a similar state-of-the-art approach that exclusively incorporates topological information of ppi networks for community detection. In addition to the high quality of the results, one main advantage of lcda-go is its low computational time complexity.

Combining sequence and Gene Ontology for protein module detection in the Weighted Network