Discovering Protein Complexes Research Articles

PPI-GA: A Novel Clustering Algorithm to Identify Protein Complexes within Protein-Protein Interaction Networks Using Genetic Algorithm

Comprehensive analysis of proteins to evaluate their genetic diversity, study their differences, and respond to the tensions is the main subject of an interdisciplinary field of study called proteomics. The main objective of the proteomics is to detect and quantify proteins and study their post-translational modifications and interactions using protein chemistry, bioinformatics, and biology. Any disturbance in proteins interactive network can act as a source for biological disorders and various diseases such as Alzheimer and cancer. Most current computational methods for discovering protein complexes are usually based on specific topological characteristics of protein-protein networks (PPI). To identify the protein complexes, in this paper, we, first, present a new encoding method to represent solutions; we then propose a new clustering algorithm based on the genetic algorithm, named PPI-GA, employing a new multiobjective quality function. The proposed algorithm is evaluated on two gold standard and real-world datasets. The result achieved demonstrates that the proposed algorithm can detect important protein complexes, and it provides more accurate results compared with state-of-the-art protein complex identification algorithms.

Discovery of Native Protein Complexes by Liquid Chromatography Followed byQuantitative Mass Spectrometry.

Discovering protein complexes in vivo is of vital importance to understand the evolution and function of biological systems. Proteomics analysis has evolved as a state-of-the-art technique in elucidating the above information. A combination of liquid chromatography (LC) and liquid chromatography coupled to shotgun mass spectrometry (LC-MS) provides the most exhaustive information in this regard. However, a significant amount of computational effort is required for the meaningful interpretation of the generated datasets. In this chapter we describe in detail the state-of-the-art pipeline to discover soluble protein complexes and provide practical advice focusing on typical situations a biologist faces while analyzing such proteomics datasets. Furthermore, we briefly describe two commonly used software packages to solve the described problem: Weka for training protein-protein interactions (PPIs) using machine learning (ML) and Cytoscape for clustering the interaction network.

A novel graph clustering method with a greedy heuristic search algorithm for mining protein complexes from dynamic and static PPI networks

Discovering protein complexes from protein-protein interaction (PPI) networks is one of the primary tasks in bioinformatics. However, most of the state-of-the-art methods still face some challenges, such as the inability to discover overlapping protein complexes, failure to consider the inherent structure of real protein complexes, and non-utilization of biological information. Based on the above mentioned aspects, we present a novel graph clustering method with a greedy heuristic search algorithm for mining protein complexes using a new clustering model in dynamic and static weighted PPI networks (named MPC-C). First, MPC-C constructed dynamic and static weighted PPI networks by combining biological and topological information. Second, initial clusters were obtained using core and multifunctional proteins, following which we proposed a greedy heuristic search algorithm to expand each initial cluster and form candidate protein complexes in dynamic and static weighted PPI networks. Finally, unreliable and highly overlapping protein complexes were discarded. To demonstrate the performance of MPC-C, we tested this method on five PPI networks and compared it with nine other effective methods. The experimental results indicate that MPC-C outperformed the other state-of-the-art methods with respect to various computational and biologically relevant metrics.

Moth–flame optimization-based algorithm with synthetic dynamic PPI networks for discovering protein complexes

The prediction of protein complex in protein–protein interaction (PPI) networks plays such a crucial role in the understanding of biological processes. This paper presents a moth–flame optimization-based protein complex prediction algorithm, called MFOC. First of all, we build the reliable weighted dynamic PPI networks by synthesizing topological and biological information. After that, we utilize a layer-by-layer scheme to find the cores of protein complexes as the flames and let the moths fly spirally around the flames to form the complexes. To be specific, the critical proteins have priority as the hearts and cores are extended by the hearts. And then we use MFOC algorithm to make the moths converge to the flames in order to obtain the protein complexes. At last, a two-step filtration operation is executed to refine the predicted protein complexes. The proposed algorithm MFOC is applied to the reliable weighted dynamic protein interaction networks including DIP, Krogan and MIPS, and the numerous comparison results show that MFOC outperforms other classic algorithms for identifying protein complexes.

Protein complex prediction via dense subgraphs and false positive analysis.

Many proteins work together with others in groups called complexes in order to achieve a specific function. Discovering protein complexes is important for understanding biological processes and predict protein functions in living organisms. Large-scale and throughput techniques have made possible to compile protein-protein interaction networks (PPI networks), which have been used in several computational approaches for detecting protein complexes. Those predictions might guide future biologic experimental research. Some approaches are topology-based, where highly connected proteins are predicted to be complexes; some propose different clustering algorithms using partitioning, overlaps among clusters for networks modeled with unweighted or weighted graphs; and others use density of clusters and information based on protein functionality. However, some schemes still require much processing time or the quality of their results can be improved. Furthermore, most of the results obtained with computational tools are not accompanied by an analysis of false positives. We propose an effective and efficient mining algorithm for discovering highly connected subgraphs, which is our base for defining protein complexes. Our representation is based on transforming the PPI network into a directed acyclic graph that reduces the number of represented edges and the search space for discovering subgraphs. Our approach considers weighted and unweighted PPI networks. We compare our best alternative using PPI networks from Saccharomyces cerevisiae (yeast) and Homo sapiens (human) with state-of-the-art approaches in terms of clustering, biological metrics and execution times, as well as three gold standards for yeast and two for human. Furthermore, we analyze false positive predicted complexes searching the PDBe (Protein Data Bank in Europe) database in order to identify matching protein complexes that have been purified and structurally characterized. Our analysis shows that more than 50 yeast protein complexes and more than 300 human protein complexes found to be false positives according to our prediction method, i.e., not described in the gold standard complex databases, in fact contain protein complexes that have been characterized structurally and documented in PDBe. We also found that some of these protein complexes have recently been classified as part of a Periodic Table of Protein Complexes. The latest version of our software is publicly available at http://doi.org/10.6084/m9.figshare.5297314.v1.

Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network

Detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Most existing clustering algorithms for discovering protein complexes are based on static protein interaction networks in which the inherent dynamics are often overlooked. We propose a novel algorithm DPC-NADPIN (Discovering Protein Complexes based on Neighbor Affinity and Dynamic Protein Interaction Network) to identify temporal protein complexes from the time course protein interaction networks. Inspired by the idea of that the tighter a protein’s neighbors inside a module connect, the greater the possibility that the protein belongs to the module, DPC-NADPIN algorithm first chooses each of the proteins with high clustering coefficient and its neighbors to consolidate into an initial cluster, and then the initial cluster becomes a protein complex by appending its neighbor proteins according to the relationship between the affinity among neighbors inside the cluster and that outside the cluster. In our experiments, DPC-NADPIN algorithm is proved to be reasonable and it has better performance on discovering protein complexes than the following state-of-the-art algorithms: Hunter, MCODE, CFinder, SPICI, and ClusterONE; Meanwhile, it obtains many protein complexes with strong biological significance, which provide helpful biological knowledge to the related researchers. Moreover, we find that proteins are assembled coordinately to form protein complexes with characteristics of temporality and spatiality, thereby performing specific biological functions.

An evolutionary restricted neighborhood search clustering approach for PPI networks

Protein–protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of a group of proteins strictly related can be useful to predict protein functions. Clustering techniques have been widely employed to detect significant biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions introduced by RNSC, besides a new one that combines them, are used by a Genetic Algorithm as fitness functions to be optimized. Experimental evaluations performed on two different groups of interactions of the budding yeast Saccharomyces cerevisiae show that the clusters obtained by the genetic approach are a larger number of those found by RNSC, though this method predicts more true complexes.

Discovering protein complexes in protein interaction networks via exploring the weak ties effect

BackgroundStudying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in biological networks and much attention has been paid to accurately predict protein complexes from the increasing amount of protein-protein interaction (PPI) data. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, failing to take into account the inherence organization within protein complex and the roles of edges. Thus, there is a critical need to investigate the possibility of discovering protein complexes using the topological information hidden in edges.ResultsTo provide an investigation of the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. We further demonstrate that there is a negative relation between the weak tie strength and the topological similarity. By using the bridges, a reliable virtual network is constructed, in which each maximal clique corresponds to the core of a complex. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. A comprehensive comparison among the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes.ConclusionsWe proved that the weak tie effect exists in the PPI network and demonstrated that the density is insufficient to characterize the topological structure of protein complexes. Furthermore, the experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms. The analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, suggesting that the roles of edges are critical in discovering protein complexes.

Discovering protein complexes from protein-protein interaction data by dense subgraph

High-throughput techniques, such as the yeast-two-hybrid system, produce mass protein-protein interaction data. The new technique makes it possible to predict protein complexes by computation. A novel method, named DSDA, has been put forward to predict protein complexes via dense subgraph because the proteins among a protein complex have a much tighter relation among them than with others. This method chooses a node with its neighbors to form the initial subgraph, and chooses a node which has the tightest relation with the subgraph according to greedy strategy, then the chosen node is added into the initial subgraph until the subgraph density is below the threshold value. The obtained subgraph is then removed from the network and the process continues until no subgraph can be detected. Compared with other algorithms, DSDA can predict not only non-overlap protein complexes but also overlap protein complexes. The experiment results show that DSDA predict as many protein complexes as possible. And in Y78K network the accuracy of DSDA is as twice times as that of RNSC and MCL.

Dynamical Systems for Discovering Protein Complexes and Functional Modules from Biological Networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-pro...

Dynamical Systems for Discovering Protein Complexes and Functional Modules from Biological Networks

Recent advances in high throughput experiments and annotations via published literature have provided a wealth of interaction maps of several biomolecular networks, including metabolic, protein-protein, and protein-DNA interaction networks. The architecture of these molecular networks reveals important principles of cellular organization and molecular functions. Analyzing such networks, i.e., discovering dense regions in the network, is an important way to identify protein complexes and functional modules. This task has been formulated as the problem of finding heavy subgraphs, the Heaviest k-Subgraph Problem (k-HSP), which itself is NP-hard. However, any method based on the k-HSP requires the parameter k and an exact solution of k-HSP may still end up as a "spurious" heavy subgraph, thus reducing its practicability in analyzing large scale biological networks. We proposed a new formulation, called the rank-HSP, and two dynamical systems to approximate its results. In addition, a novel metric, called the Standard deviation and Mean Ratio (SMR), is proposed for use in "spurious" heavy subgraphs to automate the discovery by setting a fixed threshold. Empirical results on both the simulated graphs and biological networks have demonstrated the efficiency and effectiveness of our proposal.

Protein Complexes in the Archaeon Methanothermobacter thermautotrophicus Analyzed by Blue Native/SDS-PAGE and Mass Spectrometry

Methanothermobacter thermautotrophicus is a thermophilic archaeon that produces methane as the end product of its primary metabolism. The biochemistry of methane formation has been extensively studied and is catalyzed by individual enzymes and proteins that are organized in protein complexes. Although much is known of the protein complexes involved in methanogenesis, only limited information is available on the associations of proteins involved in other cell processes of M. thermautotrophicus. To visualize and identify interacting and individual proteins of M. thermautotrophicus on a proteome-wide scale, protein preparations were separated using blue native electrophoresis followed by SDS-PAGE. A total of 361 proteins, corresponding to almost 20% of the predicted proteome, was identified using peptide mass fingerprinting after MALDI-TOF MS. All previously characterized complexes involved in energy generation could be visualized. Furthermore the expression and association of the heterodisulfide reductase and methylviologen-reducing hydrogenase complexes depended on culture conditions. Also homomeric supercomplexes of the ATP synthase stalk subcomplex and the N5-methyl-5,6,7,8-tetrahydromethanopterin:coenzyme M methyltransferase complex were separated. Chemical cross-linking experiments confirmed that the multimerization of both complexes was not experimentally induced. A considerable number of previously uncharacterized protein complexes were reproducibly visualized. These included an exosome-like complex consisting of four exosome core subunits, which associated with a tRNA-intron endonuclease, thereby expanding the constituency of archaeal exosomes. The results presented show the presence of novel complexes and demonstrate the added value of including blue native gel electrophoresis followed by SDS-PAGE in discovering protein complexes that are involved in catabolic, anabolic, and general cell processes.

FISHING FOR COMPLEXES

SEQUENCING A GENOME IS just the first step in understanding the biology of an organism. Proteins really do the work. With that in mind, two groups independendy report similar methods for discovering protein complexes within yeast. Both groups use proteins to fish out protein interactions. Yeast proteins are turned into bait by attaching a tag that allows them to be captured in an irnmunoaffinity purification. The captured complexes are then separated by gel electrophoresis, and excised gel spots are analyzed by mass spectrometry (MS). A team of scientists from MDS Proteomics in Toronto and Odense, Denmark; Mount Sinai Hospital inToronto; and the University of Toronto used the method to detect 3,617 protein interactions, starting with 725 bait proteins [Nature, 415,180 (2002)}. The other team, led by Giulio Superti-Furga, vice president of biology at Cellzome AG and team leader at the European Molecular Biology Laboratory both in Heidelberg, Germany identified 232 distinct multiprotein complexes ...