A comprehensive review and evaluation of computational methods for identifying protein complexes from protein–protein interaction networks

Protein complexes are the fundamental units for many cellular processes. Identifying protein complexes accurately is critical for understanding the functions and organizations of cells. With the increment of genome-scale protein–protein interaction (PPI) data for different species, various computational methods focus on identifying protein complexes from PPI networks. In this article, we give a comprehensive and updated review on the state-of-the-art computational methods in the field of protein complex identification, especially focusing on the newly developed approaches. The computational methods are organized into three categories, including cluster-quality-based methods, node-affinity-based methods and ensemble clustering methods. Furthermore, the advantages and disadvantages of different methods are discussed, and then, the performance of 17 state-of-the-art methods is evaluated on two widely used benchmark data sets. Finally, the bottleneck problems and their potential solutions in this important field are discussed.

Identifying protein complexes based on an edge weight algorithm and core-attachment structure

Background

Protein complex identification from protein-protein interaction (PPI) networks is crucial for understanding cellular organization principles and functional mechanisms. In recent decades, numerous computational methods have been proposed to identify protein complexes. However, most of the current state-of-the-art studies still have some challenges to resolve, including their high false-positives rates, incapability of identifying overlapping complexes, lack of consideration for the inherent organization within protein complexes, and absence of some biological attachment proteins.

Results

In this paper, to overcome these limitations, we present a protein complex identification method based on an edge weight method and core-attachment structure (EWCA) which consists of a complex core and some sparse attachment proteins. First, we propose a new weighting method to assess the reliability of interactions. Second, we identify protein complex cores by using the structural similarity between a seed and its direct neighbors. Third, we introduce a new method to detect attachment proteins that is able to distinguish and identify peripheral proteins and overlapping proteins. Finally, we bind attachment proteins to their corresponding complex cores to form protein complexes and discard redundant protein complexes. The experimental results indicate that EWCA outperforms existing state-of-the-art methods in terms of both accuracy and p-value. Furthermore, EWCA could identify many more protein complexes with statistical significance. Additionally, EWCA could have better balance accuracy and efficiency than some state-of-the-art methods with high accuracy.

Conclusions

In summary, EWCA has better performance for protein complex identification by a comprehensive comparison with twelve algorithms in terms of different evaluation metrics. The datasets and software are freely available for academic research at https://github.com/RongquanWang/EWCA.

Predicting overlapping protein complexes based on core-attachment and a local modularity structure

BACKGROUND

In recent decades, detecting protein complexes (PCs) from protein-protein interaction networks (PPINs) has been an active area of research. There are a large number of excellent graph clustering methods that work very well for identifying PCs. However, most of existing methods usually overlook the inherent core-attachment organization of PCs. Therefore, these methods have three major limitations we should concern. Firstly, many methods have ignored the importance of selecting seed, especially without considering the impact of overlapping nodes as seed nodes. Thus, there may be false predictions. Secondly, PCs are generally supposed to be dense subgraphs. However, the subgraphs with high local modularity structure usually correspond to PCs. Thirdly, a number of available methods lack handling noise mechanism, and miss some peripheral proteins. In summary, all these challenging issues are very important for predicting more biological overlapping PCs.

RESULTS

In this paper, to overcome these weaknesses, we propose a clustering method by core-attachment and local modularity structure, named CALM, to detect overlapping PCs from weighted PPINs with noises. Firstly, we identify overlapping nodes and seed nodes. Secondly, for a node, we calculate the support function between a node and a cluster. In CALM, a cluster which initially consists of only a seed node, is extended by adding its direct neighboring nodes recursively according to the support function, until this cluster forms a locally optimal modularity subgraph. Thirdly, we repeat this process for the remaining seed nodes. Finally, merging and removing procedures are carried out to obtain final predicted clusters. The experimental results show that CALM outperforms other classical methods, and achieves ideal overall performance. Furthermore, CALM can match more complexes with a higher accuracy and provide a better one-to-one mapping with reference complexes in all test datasets. Additionally, CALM is robust against the high rate of noise PPIN.

CONCLUSIONS

By considering core-attachment and local modularity structure, CALM could detect PCs much more effectively than some representative methods. In short, CALM could potentially identify previous undiscovered overlapping PCs with various density and high modularity.

Evolutionary Graph Clustering for Protein Complex Identification

This paper presents a graph clustering algorithm, called EGCPI, to discover protein complexes in protein-protein interaction (PPI) networks. In performing its task, EGCPI takes into consideration both network topologies and attributes of interacting proteins, both of which have been shown to be important for protein complex discovery. EGCPI formulates the problem as an optimization problem and tackles it with evolutionary clustering. Given a PPI network, EGCPI first annotates each protein with corresponding attributes that are provided in Gene Ontology database. It then adopts a similarity measure to evaluate how similar the connected proteins are taking into consideration the network topology. Given this measure, EGCPI then discovers a number of graph clusters within which proteins are densely connected, based on an evolutionary strategy. At last, EGCPI identifies protein complexes in each discovered cluster based on the homogeneity of attributes performed by pairwise proteins. EGCPI has been tested with several real data sets and the experimental results show EGCPI is very effective on protein complex discovery, and the evolutionary clustering is helpful to identify protein complexes in PPI networks. The software of EGCPI can be downloaded via: https://github.com/hetiantian1985/EGCPI.

Measuring Boundedness for Protein Complex Identification in PPI Networks

The problem of identifying protein complexes in Protein-Protein Interaction (PPI) networks is usually formulated as the problem of identifying dense regions in such networks. In this paper, we present a novel approach, called TBPCI, to identify protein complexes based instead on the concept of a measure of boundedness. Such a measure is defined as an objective function of a Jaccard Index-based connectedness measure which takes into consideration how much two proteins within a network are connected to each other, and an association measure which takes into consideration how much two connecting proteins are associated based on their attributes found in the Gene Ontology database. Based on the above two measures, the objective function is derived to capture how strong the proteins can be considered as bounded together and the objective value is therefore referred as the aggregated degree of boundedness. To identify protein complexes, TBPCI computes the degree of boundedness between all possible pairwise proteins. Then, TBPCI uses a Breadth-First-Search method to determine whether a protein-pair should be incorporated into the same complex. TBPCI has been tested with several real data sets and the experimental results show it is an effective approach for identifying protein complexes in PPI networks.

Predicting Protein Functions by Using Unbalanced Random Walk Algorithm on Three Biological Networks

With the gap between the sequence data and their functional annotations becomes increasing wider, many computational methods have been proposed to annotate functions for unknown proteins. However, designing effective methods to make good use of various biological resources is still a big challenge for researchers due to function diversity of proteins. In this work, we propose a new method named ThrRW, which takes several steps of random walking on three different biological networks: protein interaction network (PIN), domain co-occurrence network (DCN), and functional interrelationship network (FIN), respectively, so as to infer functional information from neighbors in the corresponding networks. With respect to the topological and structural differences of the three networks, the number of walking steps in the three networks will be different. In the course of working, the functional information will be transferred from one network to another according to the associations between the nodes in different networks. The results of experiment on S. cerevisiae data show that our method achieves better prediction performance not only than the methods that consider both PIN data and GO term similarities, but also than the methods using both PIN data and protein domain information, which verifies the effectiveness of our method on integrating multiple biological data sources.

United Complex Centrality for Identification of Essential Proteins from PPI Networks

Essential proteins are indispensable for the survival or reproduction of an organism. Identification of essential proteins is not only necessary for the understanding of the minimal requirements for cellular life, but also important for the disease study and drug design. With the development of high-throughput techniques, a large number of protein-protein interaction data are available, which promotes the studies of essential proteins from the network level. Up to now, though a series of computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a new method, United complex Centrality (UC), to identify essential proteins by integrating the protein complexes with the topological features of protein-protein interaction (PPI) networks. By analyzing the relationship between the essential proteins and the known protein complexes of S. cerevisiae and human, we find that the proteins in complexes are more likely to be essential compared with the proteins not included in any complexes and the proteins appeared in multiple complexes are more inclined to be essential compared to those only appeared in a single complex. Considering that some protein complexes generated by computational methods are inaccurate, we also provide a modified version of UC with parameter alpha, named UC-P. The experimental results show that protein complex information can help identify the essential proteins more accurate both for the PPI network of S. cerevisiae and that of human. The proposed method UC performs obviously better than the eight previously proposed methods (DC, IC, EC, SC, BC, CC, NC, and LAC) for identifying essential proteins.

ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network

Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.

Identifying hierarchical and overlapping protein complexes based on essential protein-protein interactions and "seed-expanding" method

Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on λ-module and "seed-expanding." First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a λ-module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter λ_th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of λ_th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes.

Identifying protein complexes and functional modules--from static PPI networks to dynamic PPI networks

Cellular processes are typically carried out by protein complexes and functional modules. Identifying them plays an important role for our attempt to reveal principles of cellular organizations and functions. In this article, we review computational algorithms for identifying protein complexes and/or functional modules from protein-protein interaction (PPI) networks. We first describe issues and pitfalls when interpreting PPI networks. Then based on types of data used and main ideas involved, we briefly describe protein complex and/or functional module identification algorithms in four categories: (i) those based on topological structures of unweighted PPI networks; (ii) those based on characters of weighted PPI networks; (iii) those based on multiple data integrations; and (iv) those based on dynamic PPI networks. The PPI networks are modelled increasingly precise when integrating more types of data, and the study of protein complexes would benefit by shifting from static to dynamic PPI networks.

Identifying protein complexes based on density and modularity in protein-protein interaction network

Background

Identifying protein complexes is crucial to understanding principles of cellular organization and functional mechanisms. As many evidences have indicated that the subgraphs with high density or with high modularity in PPI network usually correspond to protein complexes, protein complexes detection methods based on PPI network focused on subgraph's density or its modularity in PPI network. However, dense subgraphs may have low modularity and subgraph with high modularity may have low density, which results that protein complexes may be subgraphs with low modularity or with low density in the PPI network. As the density-based methods are difficult to mine protein complexes with low density, and the modularity-based methods are difficult to mine protein complexes with low modularity, both two methods have limitation for identifying protein complexes with various density and modularity.

Results

To identify protein complexes with various density and modularity, including those have low density but high modularity and those have low modularity but high density, we define a novel subgraph's fitness, f_ρ, as f_ρ= (density)^ρ*(modularity)^1-ρ, and propose a novel algorithm, named LF_PIN, to identify protein complexes by expanding seed edges to subgraphs with the local maximum fitness value. Experimental results of LF-PIN in S.cerevisiae show that compared with the results of fitness equal to density (ρ = 1) or equal to modularity (ρ = 0), the LF-PIN identifies known protein complexes more effectively when the fitness value is decided by both density and modularity (0<ρ<1). Compared with the results of seven competing protein complex detection methods (CMC, Core-Attachment, CPM, DPClus, HC-PIN, MCL, and NFC) in S.cerevisiae and E.coli, LF-PIN outperforms other seven methods in terms of matching with known complexes and functional enrichment. Moreover, LF-PIN has better performance in identifying protein complexes with low density or with low modularity.

Conclusions

By considering both the density and the modularity, LF-PIN outperforms other protein complexes detection methods that only consider density or modularity, especially in identifying known protein complexes with low density or low modularity.

Utilizing both topological and attribute information for protein complex identification in PPI networks

Many computational approaches developed to identify protein complexes in protein-protein interaction (PPI) networks perform their tasks based only on network topologies. The attributes of the proteins in the networks are usually ignored. As protein attributes within a complex may also be related to each other, we have developed a PCIA algorithm to take into consideration both such information and network topology in the identification process of protein complexes. Given a PPI network, PCIA first finds information about the attributes of the proteins in a PPI network in the Gene Ontology databases and uses such information for the identification of protein complexes. PCIA then computes a Degree of Association measure for each pair of interacting proteins to quantitatively determine how much their attribute values associate with each other. Based on this association measure, PCIA is able to discover dense graph clusters consisting of proteins whose attribute values are significantly closer associated with each other. PCIA has been tested with real data and experimental results seem to indicate that attributes of the proteins in the same complex do have some association with each other and, therefore, that protein complexes can be more accurately identified when protein attributes are taken into consideration.