Detection Of Protein Complexes Research Articles

Improved method for protein complex detection using bottleneck proteins

BackgroundDetecting protein complexes is one of essential and fundamental tasks in understanding various biological functions or processes. Therefore accurate identification of protein complexes is indispensable.MethodsFor more accurate detection of protein complexes, we propose an algorithm which detects dense protein sub-networks of which proteins share closely located bottleneck proteins. The proposed algorithm is capable of finding protein complexes which allow overlapping with each other.ResultsWe applied our algorithm to several PPI (Protein-Protein Interaction) networks of Saccharomyces cerevisiae and Homo sapiens, and validated our results using public databases of protein complexes. The prediction accuracy was even more improved over our previous work which used also bottleneck information of the PPI network, but showed limitation when predicting small-sized protein complex detection.ConclusionsOur algorithm resulted in overlapping protein complexes with significantly improved F1 score over existing algorithms. This result comes from high recall due to effective network search, as well as high precision due to proper use of bottleneck information during the network search.

Detecting Protein Complexes through Micro-Network Comparison in Protein-Protein Interaction Networks

Proteins are likely to form closely coupled protein complexes as functional units to participate in biological processes. We propose a Micro-Network Comparison algorithm to solve the problem of detecting protein complexes from protein interaction networks. We express the characters of a connection between a node and a core graph through the In-module Micro-network graph and Out-module Micro-network graph. We compare the number of edges in two graphs to decide whether a node has a dense or loose connection to a core graph. We give four experimental results using the algorithm and the results show that our algorithm has an excellent performance in both accuracy and hit rate.

HIV Detection: Solid‐State Nanopore Detection of Protein Complexes: Applications in Healthcare and Protein Kinetics (Small 5/2013)

HIV Detection: Solid‐State Nanopore Detection of Protein Complexes: Applications in Healthcare and Protein Kinetics (Small 5/2013)

In situ detection of individual mRNA molecules and protein complexes or post-translational modifications using padlock probes combined with the in situ proximity ligation assay

Analysis at the single-cell level is essential for the understanding of cellular responses in heterogeneous cell populations, but it has been difficult to perform because of the strict requirements put on detection methods with regard to selectivity and sensitivity (i.e., owing to the cross-reactivity of probes and limited signal amplification). Here we describe a 1.5-d protocol for enumerating and genotyping mRNA molecules in situ while simultaneously obtaining information on protein interactions or post-translational modifications; this is achieved by combining padlock probes with in situ proximity ligation assays (in situ PLA). In addition, we provide an example of how to design padlock probes and how to optimize staining conditions for fixed cells and tissue sections. Both padlock probes and in situ PLA provide the ability to directly visualize single molecules by standard microscopy in fixed cells or tissue sections, and these methods may thus be valuable for both research and diagnostic purposes.

A conservation and biophysics guided stochastic approach to refining docked multimeric proteins

BackgroundWe introduce a protein docking refinement method that accepts complexes consisting of any number of monomeric units. The method uses a scoring function based on a tight coupling between evolutionary conservation, geometry and physico-chemical interactions. Understanding the role of protein complexes in the basic biology of organisms heavily relies on the detection of protein complexes and their structures. Different computational docking methods are developed for this purpose, however, these methods are often not accurate and their results need to be further refined to improve the geometry and the energy of the resulting complexes. Also, despite the fact that complexes in nature often have more than two monomers, most docking methods focus on dimers since the computational complexity increases exponentially due to the addition of monomeric units.ResultsOur results show that the refinement scheme can efficiently handle complexes with more than two monomers by biasing the results towards complexes with native interactions, filtering out false positive results. Our refined complexes have better IRMSDs with respect to the known complexes and lower energies than those initial docked structures.ConclusionsEvolutionary conservation information allows us to bias our results towards possible functional interfaces, and the probabilistic selection scheme helps us to escape local energy minima. We aim to incorporate our refinement method in a larger framework which also enables docking of multimeric complexes given only monomeric structures.

PLW: Probabilistic Local Walks for detecting protein complexes from protein interaction networks.

BackgroundMany biological processes are carried out by proteins interacting with each other in the form of protein complexes. However, large-scale detection of protein complexes has remained constrained by experimental limitations. As such, computational detection of protein complexes by applying clustering algorithms on the abundantly available protein-protein interaction (PPI) networks is an important alternative. However, many current algorithms have overlooked the importance of selecting seeds for expansion into clusters without excluding important proteins and including many noisy ones, while ensuring a high degree of functional homogeneity amongst the proteins detected for the complexes.ResultsWe designed a novel method called Probabilistic Local Walks (PLW) which clusters regions in a PPI network with high functional similarity to find protein complex cores with high precision and efficiency in (|V| log |V| + |E|) time. A seed selection strategy, which prioritises seeds with dense neighbourhoods, was devised. We defined a topological measure, called common neighbour similarity, to estimate the functional similarity of two proteins given the number of their common neighbours.ConclusionsOur proposed PLW algorithm achieved the highest F-measure (recall and precision) when compared to 11 state-of-the-art methods on yeast protein interaction data, with an improvement of 16.7% over the next highest score. Our experiments also demonstrated that our seed selection strategy is able to increase algorithm precision when applied to three previous protein complex mining techniques.AvailabilityThe software, datasets and predicted complexes are available at http://wonglkd.github.io/PLW

Detecting overlapping protein complexes in PPI networks based on robustness

BackgroundRecently, large data sets of protein-protein interactions (PPI) which can be modeled as PPI networks are generated through high-throughput methods. And locally dense regions in PPI networks are very likely to be protein complexes. Since protein complexes play a key role in many biological processes, detecting protein complexes in PPI networks is one of important tasks in post-genomic era. However, PPI networks are often incomplete and noisy, which builds barriers to mining protein complexes.ResultsWe propose a new and effective algorithm based on robustness to detect overlapping clusters as protein complexes in PPI networks. And in order to improve the accuracy of resulting clusters, our algorithm tries to reduce bad effects brought by noise in PPI networks. And in our algorithm, each new cluster begins from a seed and is expanded through adding qualified nodes from the cluster's neighbourhood nodes. Besides, in our algorithm, a new distance measurement method between a cluster K and a node in the neighbours of K is proposed as well. The performance of our algorithm is evaluated by applying it on two PPI networks which are Gavin network and Database of Interacting Proteins (DIP). The results show that our algorithm is better than Markov clustering algorithm (MCL), Clique Percolation method (CPM) and core-attachment based method (CoAch) in terms of F-measure, co-localization and Gene Ontology (GO) semantic similarity.ConclusionsOur algorithm detects locally dense regions or clusters as protein complexes. The results show that protein complexes generated by our algorithm have better quality than those generated by some previous classic methods. Therefore, our algorithm is effective and useful.

Construction and application of dynamic protein interaction network based on time course gene expression data

In recent years, researchers have tried to inject dynamic information into static protein interaction networks (PINs). The paper first proposes a three-sigma method to identify active time points of each protein in a cellular cycle, where three-sigma principle is used to compute an active threshold for each gene according to the characteristics of its expression curve. Then a dynamic protein interaction network (DPIN) is constructed, which includes the dynamic changes of protein interactions. To validate the efficiency of DPIN, MCL, CPM, and core attachment algorithms are applied on two different DPINs, the static PIN and the time course PIN (TC-PIN) to detect protein complexes. The performance of each algorithm on DPINs outperforms those on other networks in terms of matching with known complexes, sensitivity, specificity, f-measure, and accuracy. Furthermore, the statistics of three-sigma principle show that 23-45% proteins are active at a time point and most proteins are active in about half of cellular cycle. In addition, we find 94% essential proteins are in the group of proteins that are active at equal or great than 12 timepoints of GSE4987, which indicates the potential existence of feedback mechanisms that can stabilize the expression level of essential proteins and might provide a new insight for predicting essential proteins from dynamic protein networks.

Identifying protein complexes with fuzzy machine learning model

BackgroundMany computational approaches have been developed to detect protein complexes from protein-protein interaction (PPI) networks. However, these PPI networks are always built from high-throughput experiments. The presence of unreliable interactions in PPI network makes this task very challenging.MethodsIn this study, we proposed a Genetic-Algorithm Fuzzy Naïve Bayes (GAFNB) filter to classify the protein complexes from candidate subgraphs. It takes unreliability into consideration and tackles the presence of unreliable interactions in protein complex. We first got candidate protein complexes through existed popular methods. Each candidate protein complex is represented by 29 graph features and 266 biological property based features. GAFNB model is then applied to classify the candidate complexes into positive or negative.ResultsOur evaluation indicates that the protein complex identification algorithms using the GAFNB model filtering outperform original ones. For evaluation of GAFNB model, we also compared the performance of GAFNB with Naïve Bayes (NB). Results show that GAFNB performed better than NB. It indicates that a fuzzy model is more suitable when unreliability is present.ConclusionsWe conclude that filtering candidate protein complexes with GAFNB model can improve the effectiveness of protein complex identification. It is necessary to consider the unreliability in this task.

Detection of protein complexes from affinity purification/mass spectrometry data

BackgroundRecent advances in molecular biology have led to the accumulation of large amounts of data on protein-protein interaction networks in different species. An important challenge for the analysis of these data is to extract functional modules such as protein complexes and biological processes from networks which are characterised by the present of a significant number of false positives. Various computational techniques have been applied in recent years. However, most of them treat protein interaction as binary. Co-complex relations derived from affinity purification/mass spectrometry (AP-MS) experiments have been largely ignored.MethodsThis paper presents a new algorithm for detecting protein complexes from AP-MS data. The algorithm intends to detect groups of prey proteins that are significantly co-associated with the same set of bait proteins. We first construct AP-MS data as a bipartite network, where one set of nodes consists of bait proteins and the other set is composed of prey proteins. We then calculate pair-wise similarities of bait proteins based on the number of their commonly shared neighbours. A hierarchical clustering algorithm is employed to cluster bait proteins based on the similarities and thus a set of 'seed' clusters is obtained. Starting from these 'seed' clusters, an expansion process is developed to identify prey proteins which are significantly associated with the same set of bait proteins. Then, a set of complete protein complexes is derived. In application to two real AP-MS datasets, we validate biological significance of predicted protein complexes by using curated protein complexes and well-characterized cellular component annotation from Gene Ontology (GO). Several statistical metrics have been applied for evaluation.ResultsExperimental results show that, the proposed algorithm achieves significant improvement in detecting protein complexes from AP-MS data. In comparison to the well-known MCL algorithm, our algorithm improves the accuracy rate by about 20% in detecting protein complexes in both networks and increases the F-Measure value by about 50% in Krogan_2006 network. Greater precision and better accuracy have been achieved and the identified complexes are demonstrated to match well with existing curated protein complexes.ConclusionsOur study highlights the significance of taking co-complex relations into account when extracting protein complexes from AP-MS data. The algorithm proposed in this paper can be easily extended to the analysis of other biological networks which can be conveniently represented by bipartite graphs such as drug-target networks.

Identifying protein complexes in protein–protein interaction networks by using clique seeds and graph entropy

The identification of protein complexes plays a key role in understanding major cellular processes and biological functions. Various computational algorithms have been proposed to identify protein complexes from protein-protein interaction (PPI) networks. In this paper, we first introduce a new seed-selection strategy for seed-growth style algorithms. Cliques rather than individual vertices are employed as initial seeds. After that, a result-modification approach is proposed based on this seed-selection strategy. Predictions generated by higher order clique seeds are employed to modify results that are generated by lower order ones. The performance of this seed-selection strategy and the result-modification approach are tested by using the entropy-based algorithm, which is currently the best seed-growth style algorithm to detect protein complexes from PPI networks. In addition, we investigate four pairs of strategies for this algorithm in order to improve its accuracy. The numerical experiments are conducted on a Saccharomyces cerevisiae PPI network. The group of best predictions consists of 1711 clusters, with the average f-score at 0.68 after removing all similar and redundant clusters. We conclude that higher order clique seeds can generate predictions with higher accuracy and that our improved entropy-based algorithm outputs more reasonable predictions than the original one.

Extending the MCL-CA algorithm for protein complex detection from weighted PPI networks

Background: Protein complexes are responsible for most of vital biological processes within the cell. Understanding the machinery behind these biological processes requires detection and analysis of complexes and their constituent proteins. Recent biological experiments on yeast by Gavin et al. (2006) revealed that proteins within a complex are organized into two parts: core and attachment. Based on these insights, we recently [2] proposed a new algorithm called MCL-CA, based on Markov clustering coupled with core-attachment based refinement, to detect protein complexes from yeast protein-protein interaction networks (PPI). We showed that the core-attachment based refinement significantly improved the accuracies of predicted complexes when matched with manually-curated yeast complexes. However, MCL-CA was still prone to noise (false positives) in the interaction datasets.

Solid‐State Nanopore Detection of Protein Complexes: Applications in Healthcare and Protein Kinetics

Protein conjugation provides a unique look into many biological phenomena and has been used for decades for molecular recognition purposes. In this study, the use of solid-state nanopores for the detection of gp120-associated complexes are investigated. They exhibit monovalent and multivalent binding to anti-gp120 antibody monomer and dimers. In order to investigate the feasibility of many practical applications related to nanopores, detection of specific protein complexes is attempted within a heterogeneous protein sample, and the role of voltage on complexed proteins is researched. It is found that the electric field within the pore can result in unbinding of a freely translocating protein complex within the transient event durations measured experimentally. The strong dependence of the unbinding time with voltage can be used to improve the detection capability of the nanopore system by adding an additional level of specificity that can be probed. These data provide a strong framework for future protein-specific detection schemes, which are shown to be feasible in the realm of a 'real-world' sample and an automated multidimensional method of detecting events.

Detecting Protein Complexes Based on Sequence Information in the Weighted Protein–Protein Interaction Network

Detecting Protein Complexes Based on Sequence Information in the Weighted Protein–Protein Interaction Network

The Cross-and-Capture system: A versatile tool in yeast proteomics

High-throughput technologies such as affinity purification and yeast two-hybrid (Y2H) screening have been applied to explore protein–protein interactions (PPIs) in the model organism Saccharomyces cerevisiae. The “Cross-and-Capture” system is an alternative approach for an assessment of PPIs using two arrayed collections of differentially tagged yeast strains. In its current implementation, Cross-and-Capture encompasses ∼500 endogenously tagged yeast proteins, predominantly with roles in DNA metabolism and maintenance. The tagged arrays can also serve other purposes in yeast proteomics, such as monitoring of protein expression and the detection of posttranslational protein modifications. In this article, I summarize the development of this tool and describe its application to detect protein complexes and to screen for novel PPIs.

Detection of protein complexes using a protein ranking algorithm

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. There is ample evidence that many disease mechanisms involve protein complexes, and being able to predict these complexes is important to the characterization of the relevant disease for diagnostic and treatment purposes. This article introduces a novel method for detecting protein complexes from PPI by using a protein ranking algorithm (ProRank). ProRank quantifies the importance of each protein based on the interaction structure and the evolutionarily relationships between proteins in the network. A novel way of identifying essential proteins which are known for their critical role in mediating cellular processes and constructing protein complexes is proposed and analyzed. We evaluate the performance of ProRank using two PPI networks on two reference sets of protein complexes created from Munich Information Center for Protein Sequence, containing 81 and 162 known complexes, respectively. We compare the performance of ProRank to some of the well known protein complex prediction methods (ClusterONE, CMC, CFinder, MCL, MCode and Core) in terms of precision and recall. We show that ProRank predicts more complexes correctly at a competitive level of precision and recall. The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of the proposed method.

AN EVOLUTIONARY CONSERVATION-BASED METHOD FOR REFINING AND RERANKING PROTEIN COMPLEX STRUCTURES

Detection of protein complexes and their structures is crucial for understanding their role in the basic biology of organisms. Computational docking methods can provide researchers with a good starting point for the analysis of protein complexes. However, these methods are often not accurate and their results need to be further refined to improve interface packing. In this paper, we introduce a refinement method that incorporates evolutionary information into a novel scoring function by employing Evolutionary Trace (ET)-based scores. Our method also takes Van der Waals interactions into account to avoid atomic clashes in refined structures. We tested our method on docked candidates of eight protein complexes and the results suggest that the proposed scoring function helps bias the search toward complexes with native interactions. We show a strong correlation between evolutionary-conserved residues and correct interface packing. Our refinement method is able to produce structures with better lRMSD (least RMSD) with respect to the known complexes and lower energies than initial docked structures. It also helps to filter out false-positive complexes generated by docking methods, by detecting little or no conserved residues on false interfaces. We believe this method is a step toward better ranking and prediction of protein complexes.

A methodology for detecting the orthology signal in a PPI network at a functional complex level

BackgroundStable evolutionary signal has been observed in a yeast protein-protein interaction (PPI) network. These finding suggests more connected regions of a PPI network to be potential mediators of evolutionary information. Because more connected regions of PPI networks contain functional complexes, we are motivated to exploit the orthology relation for identifying complexes that can be clearly attributed to such evolutionary signal.ResultsWe proposed a computational methodology for detecting the orthology signal present in a PPI network at a functional complex level. Specifically, we examined highly functionally coherent putative protein complexes as detected by a clustering technique in the complete yeast PPI network, in the yeast sub-network which spans only ortholog proteins as determined by a given second organism, and in yeast sub-networks induced by a set of proteins randomly selected. We proposed a filtering technique for extracting orthology-driven clusters with unique functionalities, that is, neither enriched by clusters identified using the complete yeast PPI network nor identified using random sampling. Moreover, we extracted functional categories that can be clearly attributed to the presence of evolutionary signal as described by these clusters.ConclusionsApplication of the proposed methodology to the yeast PPI network indicated that evolutionary information at a functional complex level can be retrieved from the structure of the network. In particular, we detected protein complexes whose functionality could be uniquely attributed to the evolutionary signal. Moreover, we identified functions that are over-represented in these complexes due the evolutionary signal.

Efficient and accurate greedy search methods for mining functional modules in protein interaction networks

BackgroundMost computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures.MethodsIn this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules.ResultsThe proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms.ConclusionsBased on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.

Protein Complexes Discovery Based on Protein-Protein Interaction Data via a Regularized Sparse Generative Network Model

Detecting protein complexes from protein interaction networks is one major task in the postgenome era. Previous developed computational algorithms identifying complexes mainly focus on graph partition or dense region finding. Most of these traditional algorithms cannot discover overlapping complexes which really exist in the protein-protein interaction (PPI) networks. Even if some density-based methods have been developed to identify overlapping complexes, they are not able to discover complexes that include peripheral proteins. In this study, motivated by recent successful application of generative network model to describe the generation process of PPI networks and to detect communities from social networks, we develop a regularized sparse generative network model (RSGNM), by adding another process that generates propensities using exponential distribution and incorporating Laplacian regularizer into an existing generative network model, for protein complexes identification. By assuming that the propensities are generated using exponential distribution, the estimators of propensities will be sparse, which not only has good biological interpretation but also helps to control the overlapping rate among detected complexes. And the Laplacian regularizer will lead to the estimators of propensities more smooth on interaction networks. Experimental results on three yeast PPI networks show that RSGNM outperforms six previous competing algorithms in terms of the quality of detected complexes. In addition, RSGNM is able to detect overlapping complexes and complexes including peripheral proteins simultaneously. These results give new insights about the importance of generative network models in protein complexes identification.