Detection Of Protein Complexes Research Articles

Decision Tree Based Approaches for Detecting Protein Complex in Protein Protein Interaction Network (PPI) via Link and Sequence Analysis

A network of modular protein complexes inside a cell coordinates many biological processes and is known as protein-protein interaction (PPI) network. A PPI network can be modeled as a graph, in which edges represent interactions among proteins, and sub graphs represent protein complexes. Previous methods for protein complex mining from PPI network mainly focused on few topological features like density and degree statistics based on the assumption that proteins inside a complex are highly interactive with each other and thus form dense subgraphs. While this assumption is true for some complexes, it doesn’t hold for many others. The important biological information within the protein amino acid sequences, which estimates the interacting property among two proteins for performing a specific biological function is not considered in most of the previous studies. There is a need for algorithms that consider both topological and biological features for correctly identifying protein complexes having varying topological structures and biological patterns inside a PPI network. In this paper, we present an algorithm for detecting protein complexes from interaction graphs. By using graph topological patterns and biological properties as features, we model each complex sub graph by decision tree learners. We use a training set of known complexes to construct decision trees in depth first and BEST FIRST manner using divide and conquer strategy. Splitting criterion, such as information and Gini gain are used in tree expansion process. Training set is divided into subsets and each subset is represented as a branch of tree. Pruning techniques are used to reduce the size of tree. We applied our method to protein interaction data in yeast on two benchmark data sets, i.e., MIPS and CYC2008. According to our results, decision trees achieved a considerable improvement over clique-based algorithms in terms of its ability to recover known complexes by using integrated biological and topological properties.

Observing the Assembly of Protein Complexes in Living Eukaryotic Cells in Super-Resolution Using refSOFI.

Few approaches are currently available that allow the detection of protein-protein interactions (PPIs) in super-resolution, and the observation of the assembly of protein complexes in living cells has been particularly challenging. We developed reconstituted fluorescence-based stochastic optical fluctuation imaging (refSOFI), which is based on bimolecular fluorescence complementation (BiFC) and SOFI, allowing us to detect protein complex assembly 30 min after the induction of complex formation. Here we describe how to use refSOFI to map the assembly of two proteins of interest into a complex within living cells at super-resolution.

一种新的基于多源信息的复合物识别算法

一种新的基于多源信息的复合物识别算法

一种新的基于多源信息的复合物识别算法

一种新的基于多源信息的复合物识别算法

Detection of native proteins using solid-substrate electrospray ionization mass spectrometry with nonpolar solvents

Detection of native proteins, particularly directly from raw biological samples, has been a challenging task for mass spectrometry. In this study, we demonstrated that solid-substrate electrospray ionization mass spectrometry with nonpolar solvents such as n-hexane could allow detection of native proteins and protein complexes directly from raw biological samples. Mechanistic study revealed that the process involved rapid vaporization of the nonpolar solvent, temperature reduction of substrate surface, condensation of water from the ambient air, and spray ionization of the condensed water with analytes under the electric field. The fine spray with water at low temperature allowed the technique to detect native proteins, even directly from viscous samples (e.g., egg white) and solid samples (e.g., bone marrow). This study sheds new insight into the sampling and ionization process of mass spectrometry and provides a technique of great potential for characterization of proteins.

A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.

Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information. The metric reflects its representability in a larger local neighborhood to a cluster of a protein interaction (PPI) network. Based on the metric, we propose a seed-expansion graph clustering algorithm (SEGC) for protein complexes detection in PPI networks. A roulette wheel strategy is used in the selection of the seed to enhance the diversity of clustering. For a candidate node u, we define its closeness to a cluster C, denoted as NC(u, C), by combing the density of a cluster C and the connection between a node u and C. In SEGC, a cluster which initially consists of only a seed node, is extended by adding nodes recursively from its neighbors according to the closeness, until all neighbors fail the process of expansion. We compare the F-measure and accuracy of the proposed SEGC algorithm with other algorithms on Saccharomyces cerevisiae protein interaction networks. The experimental results show that SEGC outperforms other algorithms under full coverage.

The impact of protein interaction networks’ characteristics on computational complex detection methods

Protein complexes of physically interacting proteins play an important role in organizing various biological processes in the cell. Therefore, correctly identifying complexes is useful for deciphering the cellular mechanisms underlying many biological processes. Since the existing high-throughput techniques have produced a large amount of protein interactions, computational methods are useful complements to the experimental methods for detecting protein complexes. In this paper, we analyze six protein interaction networks widely used for protein complex detection, and compare the performance of six classic computational methods on them in order to find the impacts of network characteristics on the performances of these complex detection methods. Furthermore, we change topological characteristics of six protein interaction networks, and verify the findings by testing performances of six methods on new ones. We hope our study will not only help recognize the relations between characteristics of protein interaction networks and computational complex detection methods, but also provide valuable insight to improve the performance in protein complex detection area.

CPredictor3.0: detecting protein complexes from PPI networks with expression data and functional annotations

BackgroundEffectively predicting protein complexes not only helps to understand the structures and functions of proteins and their complexes, but also is useful for diagnosing disease and developing new drugs. Up to now, many methods have been developed to detect complexes by mining dense subgraphs from static protein-protein interaction (PPI) networks, while ignoring the value of other biological information and the dynamic properties of cellular systems.ResultsIn this paper, based on our previous works CPredictor and CPredictor2.0, we present a new method for predicting complexes from PPI networks with both gene expression data and protein functional annotations, which is called CPredictor3.0. This new method follows the viewpoint that proteins in the same complex should roughly have similar functions and are active at the same time and place in cellular systems. We first detect active proteins by using gene express data of different time points and cluster proteins by using gene ontology (GO) functional annotations, respectively. Then, for each time point, we do set intersections with one set corresponding to active proteins generated from expression data and the other set corresponding to a protein cluster generated from functional annotations. Each resulting unique set indicates a cluster of proteins that have similar function(s) and are active at that time point. Following that, we map each cluster of active proteins of similar function onto a static PPI network, and get a series of induced connected subgraphs. We treat these subgraphs as candidate complexes. Finally, by expanding and merging these candidate complexes, the predicted complexes are obtained.We evaluate CPredictor3.0 and compare it with a number of existing methods on several PPI networks and benchmarking complex datasets. The experimental results show that CPredictor3.0 achieves the highest F1-measure, which indicates that CPredictor3.0 outperforms these existing method in overall.ConclusionCPredictor3.0 can serve as a promising tool of protein complex prediction.

A New Method of Constructing a Weighted PPI Network

In order to filter out the PPI (protein-protein interaction) data noise and improve the accuracy of protein complexes prediction, this paper proposed a method to integrate biological information and topology information as the weight of the PPI network. There are also some traditional methods to deal with the data noise by topology information of biological information, but fewer of them integrate those two aspect. In this paper, we introduced a method to extract the feature vectors for protein couples or the interactions instead of single proteins based on both topology and biology information, then the cosine theory is used to measure the weight of PPI network. At last, it used four classical predicting protein complexes algorithms to detect protein complexes from different weighted PPI network. The result shows that our method performs better both on filtering out data noise and predicting protein complexes.

Detecting protein complexes based on a combination of topological and biological properties in protein-protein interaction network.

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called CSC to detect protein complexes. The method is evaluated in terms of positive predictive value, sensitivity and accuracy using the datasets of the model organism, yeast and humans. CSC outperforms several other competing algorithms for both organisms. Further, we present a framework to establish the usefulness of CSC in analyzing the influence of a given disease gene in a complex topologically as well as biologically considering eight major association factors.

A multi-network clustering method for detecting protein complexes from multiple heterogeneous networks

BackgroundThe accurate identification of protein complexes is important for the understanding of cellular organization. Up to now, computational methods for protein complex detection are mostly focus on mining clusters from protein-protein interaction (PPI) networks. However, PPI data collected by high-throughput experimental techniques are known to be quite noisy. It is hard to achieve reliable prediction results by simply applying computational methods on PPI data. Behind protein interactions, there are protein domains that interact with each other. Therefore, based on domain-protein associations, the joint analysis of PPIs and domain-domain interactions (DDI) has the potential to obtain better performance in protein complex detection. As traditional computational methods are designed to detect protein complexes from a single PPI network, it is necessary to design a new algorithm that could effectively utilize the information inherent in multiple heterogeneous networks.ResultsIn this paper, we introduce a novel multi-network clustering algorithm to detect protein complexes from multiple heterogeneous networks. Unlike existing protein complex identification algorithms that focus on the analysis of a single PPI network, our model can jointly exploit the information inherent in PPI and DDI data to achieve more reliable prediction results. Extensive experiment results on real-world data sets demonstrate that our method can predict protein complexes more accurately than other state-of-the-art protein complex identification algorithms.ConclusionsIn this work, we demonstrate that the joint analysis of PPI network and DDI network can help to improve the accuracy of protein complex detection.

A common neighbor based technique to detect protein complexes in PPI networks.

Detection of protein complexes by analyzing and understanding PPI networks is an important task and critical to all aspects of cell biology. We present a technique called PROtein COmplex DEtection based on common neighborhood (PROCODE) that considers the inherent organization of protein complexes as well as the regions with heavy interactions in PPI networks to detect protein complexes. Initially, the core of the protein complexes is detected based on the neighborhood of PPI network. Then a merging strategy based on density is used to attach proteins and protein complexes to the core-protein complexes to form biologically meaningful structures. The predicted protein complexes of PROCODE was evaluated and analyzed using four PPI network datasets out of which three were from budding yeast and one from human. Our proposed technique is compared with some of the existing techniques using standard benchmark complexes and PROCODE was found to match very well with actual protein complexes in the benchmark data. The detected complexes were at par with existing biological evidence and knowledge.

Identifying protein complex by integrating characteristic of core-attachment into dynamic PPI network.

How to identify protein complex is an important and challenging task in proteomics. It would make great contribution to our knowledge of molecular mechanism in cell life activities. However, the inherent organization and dynamic characteristic of cell system have rarely been incorporated into the existing algorithms for detecting protein complexes because of the limitation of protein-protein interaction (PPI) data produced by high throughput techniques. The availability of time course gene expression profile enables us to uncover the dynamics of molecular networks and improve the detection of protein complexes. In order to achieve this goal, this paper proposes a novel algorithm DCA (Dynamic Core-Attachment). It detects protein-complex core comprising of continually expressed and highly connected proteins in dynamic PPI network, and then the protein complex is formed by including the attachments with high adhesion into the core. The integration of core-attachment feature into the dynamic PPI network is responsible for the superiority of our algorithm. DCA has been applied on two different yeast dynamic PPI networks and the experimental results show that it performs significantly better than the state-of-the-art techniques in terms of prediction accuracy, hF-measure and statistical significance in biology. In addition, the identified complexes with strong biological significance provide potential candidate complexes for biologists to validate.

Identifying direct contacts between protein complex subunits from their conditional dependence in proteomics datasets

Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method’s value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available.

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.

Protein Complexes Prediction Method Based on Core-Attachment Structure and Functional Annotations.

Recent advances in high-throughput laboratory techniques captured large-scale protein–protein interaction (PPI) data, making it possible to create a detailed map of protein interaction networks, and thus enable us to detect protein complexes from these PPI networks. However, most of the current state-of-the-art studies still have some problems, for instance, incapability of identifying overlapping clusters, without considering the inherent organization within protein complexes, and overlooking the biological meaning of complexes. Therefore, we present a novel overlapping protein complexes prediction method based on core–attachment structure and function annotations (CFOCM), which performs in two stages: first, it detects protein complex cores with the maximum value of our defined cluster closeness function, in which the proteins are also closely related to at least one common function. Then it appends attach proteins into these detected cores to form the returned complexes. For performance evaluation, CFOCM and six classical methods have been used to identify protein complexes on three different yeast PPI networks, and three sets of real complexes including the Munich Information Center for Protein Sequences (MIPS), the Saccharomyces Genome Database (SGD) and the Catalogues of Yeast protein Complexes (CYC2008) are selected as benchmark sets, and the results show that CFOCM is indeed effective and robust for achieving the highest F-measure values in all tests.

CytoCluster: A Cytoscape Plugin for Cluster Analysis and Visualization of Biological Networks.

Nowadays, cluster analysis of biological networks has become one of the most important approaches to identifying functional modules as well as predicting protein complexes and network biomarkers. Furthermore, the visualization of clustering results is crucial to display the structure of biological networks. Here we present CytoCluster, a cytoscape plugin integrating six clustering algorithms, HC-PIN (Hierarchical Clustering algorithm in Protein Interaction Networks), OH-PIN (identifying Overlapping and Hierarchical modules in Protein Interaction Networks), IPCA (Identifying Protein Complex Algorithm), ClusterONE (Clustering with Overlapping Neighborhood Expansion), DCU (Detecting Complexes based on Uncertain graph model), IPC-MCE (Identifying Protein Complexes based on Maximal Complex Extension), and BinGO (the Biological networks Gene Ontology) function. Users can select different clustering algorithms according to their requirements. The main function of these six clustering algorithms is to detect protein complexes or functional modules. In addition, BinGO is used to determine which Gene Ontology (GO) categories are statistically overrepresented in a set of genes or a subgraph of a biological network. CytoCluster can be easily expanded, so that more clustering algorithms and functions can be added to this plugin. Since it was created in July 2013, CytoCluster has been downloaded more than 9700 times in the Cytoscape App store and has already been applied to the analysis of different biological networks. CytoCluster is available from http://apps.cytoscape.org/apps/cytocluster.

Identifying protein complexes in PPI network using non-cooperative sequential game

Identifying protein complexes from protein-protein interaction (PPI) network is an important and challenging task in computational biology as it helps in better understanding of cellular mechanisms in various organisms. In this paper we propose a noncooperative sequential game based model for protein complex detection from PPI network. The key hypothesis is that protein complex formation is driven by mechanism that eventually optimizes the number of interactions within the complex leading to dense subgraph. The hypothesis is drawn from the observed network property named small world. The proposed multi-player game model translates the hypothesis into the game strategies. The Nash equilibrium of the game corresponds to a network partition where each protein either belong to a complex or form a singleton cluster. We further propose an algorithm to find the Nash equilibrium of the sequential game. The exhaustive experiment on synthetic benchmark and real life yeast networks evaluates the structural as well as biological significance of the network partitions.

Protein Complex Detection by Seed-expansion Method Based on Random Walk with Restart

Protein Complex Detection by Seed-expansion Method Based on Random Walk with Restart

Neighbor Affinity-Based Core-Attachment Method to Detect Protein Complexes in Dynamic 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.