Detection Of Protein Complexes Research Articles

Protein complex detection algorithm based on multiple topological characteristics in PPI networks

Detecting protein complexes from available protein–protein interaction (PPI) networks is an important task, and several related algorithms have been proposed. These algorithms usually consider a single topological metric and ignore the rich topological characteristics and inherent organization information of protein complexes. However, the effective use of such information is crucial to protein complex detection. To overcome this deficiency, this study presents a heuristic clustering algorithm to identify protein complexes by fully exploiting the topological information of PPI networks. By considering the clustering coefficient and the node degree, a new nodal metric is proposed to quantify the importance of each node within a local subgraph. An iterative paradigm is used to incrementally identify seed proteins and expand each seed to a cluster. First, among the unclustered nodes, the node with the highest nodal metric is selected as a new seed. Then, the seed is expanded to a cluster by adding candidate nodes recursively from its neighbors according to both the density of the cluster and the connection between a candidate node and the cluster. The experimental results demonstrate that the proposed algorithm outperforms other competing algorithms in terms of F-measure and accuracy.

Protein complex detection based on flower pollination mechanism in multi-relation reconstructed dynamic protein networks

BackgroundDetecting protein complex in protein-protein interaction (PPI) networks plays a significant part in bioinformatics field. It enables us to obtain the better understanding for the structures and characteristics of biological systems.MethodsIn this study, we present a novel algorithm, named Improved Flower Pollination Algorithm (IFPA), to identify protein complexes in multi-relation reconstructed dynamic PPI networks. Specifically, we first introduce a concept called co-essentiality, which considers the protein essentiality to search essential interactions, Then, we devise the multi-relation reconstructed dynamic PPI networks (MRDPNs) and discover the potential cores of protein complexes in MRDPNs. Finally, an IFPA algorithm is put forward based on the flower pollination mechanism to generate protein complexes by simulating the process of pollen find the optimal pollination plants, namely, attach the peripheries to the corresponding cores.ResultsThe experimental results on three different datasets (DIP, MIPS and Krogan) show that our IFPA algorithm is more superior to some representative methods in the prediction of protein complexes.ConclusionsOur proposed IFPA algorithm is powerful in protein complex detection by building multi-relation reconstructed dynamic protein networks and using improved flower pollination algorithm. The experimental results indicate that our IFPA algorithm can obtain better performance than other methods.

Accurately Detecting Protein Complexes by Graph Embedding and Combining Functions with Interactions.

Identifying protein complexes is helpful for understanding cellular functions and designing drugs. In the last decades, many computational methods have been proposed based on detecting dense subgraphs or subnetworks in Protein-Protein Interaction Networks (PINs). However, the high rate of false positive/negative interactions in PINs prevents from the achievement of satisfactory detection results directly from PINs, because most of such existing methods exploit mainly topological information to do network partitioning. In this paper, we propose a new approach for protein complex detection by merging topological information of PINs and functional information of proteins. We first split proteins to a number of protein groups from the perspective of protein functions by using FunCat data. Then, for each of the resulting protein groups, we calculate two protein-protein similarity matrices: one is computed by using graph embedding over a PIN, the other is by using GO terms, and combine these two matrices to get an integrated similarity matrix. Following that, we cluster the proteins in each group based on the corresponding integrated similarity matrix, and obtain a number of small protein clusters. We map these clusters of proteins onto the PIN, and get a number of connected subgraphs. After a round of merging of overlapping subgraphs, finally we get the detected complexes. We conduct empirical evaluation on four PPI datasets (Collins, Gavin, Krogan, and Wiphi) with two complex benchmarks (CYC2008 and MIPS). Experimental results show that our method performs better than the state-of-the-art methods.

Probing Heterogeneous Lipid Interactions with Membrane Proteins Using Mass Spectrometry.

Native mass spectrometry (Native MS) enables the detection of intact membrane protein complexes in the gas phase. Membrane proteins are encapsulated in nonionic detergent micelles that protect them during transfer into the gas phase and preserves structure and noncovalent interactions. Herein, we describe methods to gently transfer membrane protein complexes bound to a mixture of heterogeneous lipid species into the gas phase. Through careful titrations, equilibrium dissociation constants can be directly determined to elucidate lipid interactions that induce positive, neutral, or negative allostery. These methods can lead to the identification of lipids that modulate membrane protein structure and function.

A statistical approach to detect protein complexes at X-ray free\xa0electron laser facilities

The Flash X-ray Imaging (FXI) technique, under development at X-ray free electron lasers (XFEL), aims to achieve structure determination based on diffraction from individual macromolecular complexes. We report an FXI study on the first protein complex—RNA polymerase II—ever injected at an XFEL. A successful 3D reconstruction requires a high number of observations of the sample in various orientations. The measured diffraction signal for many shots can be comparable to background. Here we present a robust and highly sensitive hit-identification method based on automated modeling of beamline background through photon statistics. It can operate at controlled false positive hit-rate of 3 × 10−5. We demonstrate its power in determining particle hits and validate our findings against an independent hit-identification approach based on ion time-of-flight spectra. We also validate the advantages of our method over simpler hit-identification schemes via tests on other samples and using computer simulations, showing a doubled hit-identification power.

A new evolutionary algorithm with locally assisted heuristic for complex detection in protein interaction networks

The detection of protein complexes is an essential NP-hard problem in protein-protein interaction networks (PPI). Modularity, community score, ratio cut, and internal density are some examples of the state-of-the-art optimization models. The contribution of this paper is to develop a heuristic approach that can serve as a common locally assisted optimization model to reinforce the reliability of all these complex detection models. The foundation of the proposed heuristic approach hypothesizes a possible decomposition of a pair of proteins, according to their interactions, into two different types. A pair of proteins is classified as either intra-delineation pair or inter-delineation pair depending on their topological similarity. Two proteins with high topological similarity are favored to form an intra-delineation structure; otherwise, they can form an inter-delineation pair. Therefore, the detection for a better complex structure should express more intra-delineation pairs and less inter-delineation pairs within complexes while more inter-delineation pairs and less intra-delineation pairs among separate complexes. The proposed heuristic operator is then injected into the framework of single objective and multi-objective evolutionary algorithms (EAs) while existing complex detection models work as EA optimization templates. In the experiments, we analyze the performance of the detection models when applied to the publicly available yeast protein networks. Results give clear argument to the positive impact of the proposed heuristic approach to considerably improve the detection ability of the existing optimization models, and further to provide more competitive results than a recently developed heuristic approach.

Identifying protein complexes based on node embeddings obtained from protein-protein interaction networks

BackgroundProtein complexes are one of the keys to deciphering the behavior of a cell system. During the past decade, most computational approaches used to identify protein complexes have been based on discovering densely connected subgraphs in protein-protein interaction (PPI) networks. However, many true complexes are not dense subgraphs and these approaches show limited performances for detecting protein complexes from PPI networks.ResultsTo solve these problems, in this paper we propose a supervised learning method based on network node embeddings which utilizes the informative properties of known complexes to guide the search process for new protein complexes. First, node embeddings are obtained from human protein interaction network. Then the protein interactions are weighted through the similarities between node embeddings. After that, the supervised learning method is used to detect protein complexes. Then the random forest model is used to filter the candidate complexes in order to obtain the final predicted complexes. Experimental results on real human and yeast protein interaction networks show that our method effectively improves the performance for protein complex detection.ConclusionsWe provided a new method for identifying protein complexes from human and yeast protein interaction networks, which has great potential to benefit the field of protein complex detection.

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

BackgroundIn 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.ResultsIn 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.ConclusionsBy 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.

Reconstruction of the Protein-Protein Interaction Network for Protein Complexes Identification by Walking on the Protein Pair Fingerprints Similarity Network.

Identifying protein complexes from protein-protein interaction networks (PPINs) is important to understand the science of cellular organization and function. However, PPINs produced by high-throughput studies have high false discovery rate and only represent snapshot interaction information. Reconstructing higher quality PPINs is essential for protein complex identification. Here we present a Multi-Level PPINs reconstruction (MLPR) method for protein complexes detection. From existing PPINs, we generated full combinations of every two proteins. These protein pairs are represented as a vector which includes six different sources. Then the protein pairs with same vector are mapped to the same fingerprint ID. A fingerprint similarity network is constructed next, in which a vertex represents a protein pair fingerprint ID and each vertex is connected to its top 10 similar fingerprints by edges. After random walking on the fingerprints similarity network, each vertex got a score at the steady state. According to the score of protein pairs, we considered the top ranked ones as reliable PPI and the score as the weight of edge between two distinct proteins. Finally, we expanded clusters starting from seeded vertexes based on the new weighted reliable PPINs. Applying our method on the yeast PPINs, our algorithm achieved higher F-value in protein complexes detection than the-state-of-the-art methods. The interactions in our reconstructed PPI network have more significant biological relevance than the exiting PPI datasets, assessed by gene ontology. In addition, the performance of existing popular protein complexes detection methods are significantly improved on our reconstructed network.

PCD-DPPI: Protein complex detection from dynamic PPI using shuffled frog-leaping algorithm

In the post-genome period, the identification of a protein complex from large PPI (protein-protein interaction) networks is a challenging task. Protein complexes plays a vital character in numerous molecular processes of the cell. Although, the maximum computational techniques of protein complex detection have aimed at static PPI networks that cannot represent the logical dynamicity of protein interactions. In recent times, the dynamicity of PPI networks has been exploited by building a set of dynamic PPI subnetworks with respect to every time-point in a gene expression matrix. This paper presents a new technique, called Protein Complex Detection based on Dynamic PPI (PCD-DPPI) to accomplish dynamicity in protein complex detection. The initial stage of the proposed technique is based on shuffled frog-leaping algorithm, an Optimization technique that take out a few sets of genes that are co-regulated beneath some conditions from the input gene expression matrix. An individual extracted gene set is defined as a bicluster. In the second stage, depending on the biclusters, few dynamic PPI subnetworks are taken out from the input static PPI network. By employing a complex detection approach on every dynamic PPI subnetworks, protein complexes are identified and the result is aggregated. The proposed and existing algorithms were applied to various datasets such as DIP, Krogan “Extended”, Krogan “Core”, Gavin2, Gavin6, PPI 1, Collins and Gavin + Krogan. Experimental results have been proved that the proposed PCD-DPPI efficiently depicts the dynamicity characteristics in static PPI networks and attains expressively better results when compared to other existing standard methods.

Efficiently Detecting Protein Complexes from Protein Interaction Networks via Alternating Direction Method of Multipliers.

Protein complexes are crucial in improving our understanding of the mechanisms employed by proteins. Various computational algorithms have thus been proposed to detect protein complexes from protein interaction networks. However, given massive protein interactome data obtained by high-throughput technologies, existing algorithms, especially those with additionally consideration of biological information of proteins, either have low efficiency in performing their tasks or suffer from limited effectiveness. For addressing this issue, this work proposes to detect protein complexes from a protein interaction network with high efficiency and effectiveness. To do so, the original detection task is first formulated into an optimization problem according to the intuitive properties of protein complexes. After that, the framework of alternating direction method of multipliers is applied to decompose this optimization problem into several subtasks, which can be subsequently solved in a separate and parallel manner. An algorithm for implementing this solution is then developed. Experimental results on five large protein interaction networks demonstrated that compared to state-of-the-art protein complex detection algorithms, our algorithm outperformed them in terms of both effectiveness and efficiency. Moreover, as number of parallel processes increases, one can expect an even higher computational efficiency for the proposed algorithm with no compromise on effectiveness.

A Method for Predicting Protein Complexes from Dynamic Weighted Protein-Protein Interaction Networks.

Predicting protein complexes from protein-protein interaction (PPI) network is of great significance to recognize the structure and function of cells. A protein may interact with different proteins under different time or conditions. Existing approaches only utilize static PPI network data that may lose much temporal biological information. First, this article proposed a novel method that combines gene expression data at different time points with traditional static PPI network to construct different dynamic subnetworks. Second, to further filter out the data noise, the semantic similarity based on gene ontology is regarded as the network weight together with the principal component analysis, which is introduced to deal with the weight computing by three traditional methods. Third, after building a dynamic PPI network, a predicting protein complexes algorithm based on "core-attachment" structural feature is applied to detect complexes from each dynamic subnetworks. Finally, it is revealed from the experimental results that our method proposed in this article performs well on detecting protein complexes from dynamic weighted PPI networks.

Ultrastructural localisation of protein interactions using conditionally stable nanobodies.

We describe the development and application of a suite of modular tools for high-resolution detection of proteins and intracellular protein complexes by electron microscopy (EM). Conditionally stable GFP- and mCherry-binding nanobodies (termed csGBP and csChBP, respectively) are characterized using a cell-free expression and analysis system and subsequently fused to an ascorbate peroxidase (APEX) enzyme. Expression of these cassettes alongside fluorescently labelled proteins results in recruitment and stabilisation of APEX, whereas unbound APEX nanobodies are efficiently degraded by the proteasome. This greatly simplifies correlative analyses, enables detection of less-abundant proteins, and eliminates the need to balance expression levels between fluorescently labelled and APEX nanobody proteins. Furthermore, we demonstrate the application of this system to bimolecular complementation (‘EM split-fluorescent protein’), for localisation of protein–protein interactions at the ultrastructural level.

Multi-Dimensional Scaling based grouping of known complexes and intelligent protein complex detection

Protein–Protein Interactions (PPI) play a vital role in cellular processes and are formed because of thousands of interactions among proteins. Advancements in proteomics technologies have resulted in huge PPI datasets that need to be systematically analyzed. Protein complexes are the locally dense regions in PPI networks, which extend important role in metabolic pathways and gene regulation. In this work, a novel two-phase protein complex detection and grouping mechanism is proposed. In the first phase, topological and biological features are extracted for each complex, and prediction performance is investigated using Bagging based Ensemble classifier (PCD-BEns). Performance evaluation through cross validation shows improvement in comparison to CDIP, MCode, CFinder and PLSMC methods Second phase employs Multi-Dimensional Scaling (MDS) for the grouping of known complexes by exploring inter complex relations.It is experimentally observed that the combination of topological and biological features in the proposed approach has greatly enhanced prediction performance for protein complex detection, which may help to understand various biological processes, whereas application of MDS based exploration may assist in grouping potentially similar complexes.

Predicting protein complexes using a supervised learning method combined with local structural information.

The existing protein complex detection methods can be broadly divided into two categories: unsupervised and supervised learning methods. Most of the unsupervised learning methods assume that protein complexes are in dense regions of protein-protein interaction (PPI) networks even though many true complexes are not dense subgraphs. Supervised learning methods utilize the informative properties of known complexes; they often extract features from existing complexes and then use the features to train a classification model. The trained model is used to guide the search process for new complexes. However, insufficient extracted features, noise in the PPI data and the incompleteness of complex data make the classification model imprecise. Consequently, the classification model is not sufficient for guiding the detection of complexes. Therefore, we propose a new robust score function that combines the classification model with local structural information. Based on the score function, we provide a search method that works both forwards and backwards. The results from experiments on six benchmark PPI datasets and three protein complex datasets show that our approach can achieve better performance compared with the state-of-the-art supervised, semi-supervised and unsupervised methods for protein complex detection, occasionally significantly outperforming such methods.

Monitoring ligand-dependent assembly of receptor ternary complexes in live cells by BRETFect

There is currently an unmet need for versatile techniques to monitor the assembly and dynamics of ternary complexes in live cells. Here we describe bioluminescence resonance energy transfer with fluorescence enhancement by combined transfer (BRETFect), a high-throughput technique that enables robust spectrometric detection of ternary protein complexes based on increased energy transfer from a luciferase to a fluorescent acceptor in the presence of a fluorescent intermediate. Its unique donor-intermediate-acceptor relay system is designed so that the acceptor can receive energy either directly from the donor or indirectly via the intermediate in a combined transfer, taking advantage of the entire luciferase emission spectrum. BRETFect was used to study the ligand-dependent cofactor interaction properties of the estrogen receptors ERα and ERβ, which form homo- or heterodimers whose distinctive regulatory properties are difficult to dissect using traditional methods. BRETFect uncovered the relative capacities of hetero- vs. homodimers to recruit receptor-specific cofactors and regulatory proteins, and to interact with common cofactors in the presence of receptor-specific ligands. BRETFect was also used to follow the assembly of ternary complexes between the V2R vasopressin receptor and two different intracellular effectors, illustrating its use for dissection of ternary protein-protein interactions engaged by G protein-coupled receptors. Our results indicate that BRETFect represents a powerful and versatile technique to monitor the dynamics of ternary interactions within multimeric complexes in live cells.

Electron Microscopic Detection of Proteins and Protein Complexes in Mammalian Cells Using APEX-tagged, Conditionally Stable Nanobodies.

The recent development of 3D electron microscopic techniques for cells and tissues has necessitated the development of new methods for the detection of proteins and protein-complexes in situ. The development of new genetic tags, such as the ascorbate peroxidase, APEX, for electron microscopic detection of tagged proteins has expanded the available toolbox and ushered in a new era in biological electron microscopy. Here, we describe methods for combining conditionally-stable nanobodies to fluorescent protein tags with APEX-based detection. These methods are compatible with detection of low levels of expression of fluorescently-tagged proteins and with detection of protein complexes using split GFP-based complementation methods. We describe a simple protocol for applying these methods to the electron microscopic detection of proteins and protein complexes in cultured cells.

Detection of E2F-DNA Complexes Using Chromatin Immunoprecipitation Assays.

Chromatin immunoprecipitation (ChIP), originally developed by John T. Lis and David Gilmour in 1984, has been useful to detect DNA sequences where protein(s) of interest bind. ChIP is comprised of several steps: (1) cross-linking of proteins to target DNA sequences, (2) breaking genomic DNA into 300-1000bp pieces by sonication or nuclease digestion, (3) immunoprecipitation of protein bound to target DNA with an antibody, (4) reverse cross-linking between target DNA and the bound protein to liberate the DNA fragments, and (5) amplification of target DNA fragment by PCR. Since then, the technology has evolved significantly to allow not only amplifying target sequences by PCR, but also sequencing all DNA fragment bound to a target protein, using a variant of the approach called the ChIP-seq technique (1). Another variation, the ChIP-on-ChIP, allows the detection of protein complexes bound to specific DNA sequences (2).

CPredictor 4.0: effectively detecting protein complexes in weighted dynamic PPI networks

CPredictor 4.0: effectively detecting protein complexes in weighted dynamic PPI networks

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.