Detection Of Protein Complexes Research Articles

Dopcc: Detecting overlapping protein complexes via multi-metrics and co-core attachment method.

Identification of protein complex is an important issue in the field of system biology, which is crucial to understanding the cellular organization and inferring protein functions. Recently, many computational methods have been proposed to detect protein complexes from protein-protein interaction (PPI) networks. However, most of these methods only focus on local information of proteins in the PPI network, which are easily affected by the noise in the PPI network. Meanwhile, it's still challenging to detect protein complexes, especially for overlapping cases. To address these issues, we propose a new method, named Dopcc, to detect overlapping protein complexes by constructing a multi-metrics network according to different strategies. First, we adopt the Jaccard coefficient to measure the neighbor similarity between proteins and denoise the PPI network. Then, we propose a new strategy, integrating hierarchical compressing with network embedding, to capture the high-order structural similarity between proteins. Further, a new co-core attachment strategy is proposed to detect overlapping protein complexes from multi-metrics. The experimental results show that our proposed method, Dopcc, outperforms the other eight state-of-the-art methods in terms of F-measure, MMR, and Composite Score on two yeast datasets. The source code and datasets can be downloaded from https://github.com/CSUBioGroup/Dopcc.

Overlapping complexes detection within protein interaction networks using improved genetic algorithm

BackgroundThe protein complexes detection has an important role in analyzing biological processes and understanding life at the cellular level. Although many complex detection algorithms have been proposed by employing specific topological attributes of protein interaction networks, most of them focus on detecting separated protein complexes, where each protein can belong to only one complex. Generally, many protein interaction networks have many complexes or modules that are often overlapped with each other. Developing overlapping protein complex detection algorithm thus becomes necessary. MethodsIn this respect, this paper proposes a new overlapping detection algorithm based on GA (Genetic Algorithm) to discover overlapping protein complexes. The proposed algorithm is supplied with a new local search operator based on gene ontology annotations in order to direct the search process towards discovering overlapping protein complexes that are hyper-connected and biologically related. The proposed algorithm is assessed on two gold standard and real-world datasets. ResultsThe result attained shows that the proposed algorithm can detect important overlapping protein complexes, and presents more accurate results compared to the state-of-the-art overlapping protein complexes detection algorithms. ConclusionsThe superior performance of the proposed algorithm compared with other counterpart algorithms is attributed to the fact that the proposed algorithm is a population based stochastic search algorithm which efficiently explored the search landscape to capture dense overlapping subgraphs by employing the topological properties of network, and then enhancing the obtained solution by investing gene ontology information in developing a new local search operator inside the GA framework.

Topology-Based Modularity and Modularity Density for Detecting Protein Complexes: A Comparative Study

Binary relations or interactions among bio-entities, such as proteins, set up the essential part of any living biological system. Protein-protein interactions are usually structured in a graph data structure called "protein-protein interaction networks" (PPINs). Analysis of PPINs into complexes tries to lay out the significant knowledge needed to answer many unresolved questions, including how cells are organized and how proteins work. However, complex detection problems fall under the category of non-deterministic polynomial-time hard (NP-Hard) problems due to their computational complexity. To accommodate such combinatorial explosions, evolutionary algorithms (EAs) are proven effective alternatives to heuristics in solving NP-hard problems. The main aim of this study is to make a close examination of the performance of the EAs where modularity and modularity density are selected as two different objective functions. Topology-based modularity and topology-based modularity density are designed to examine the detection ability of the EAs and to compare their performance. To conduct the experiments, two yeast Saccharomyces cerevisiae PPINs are used and evaluated under nine evaluation metrics. The results reveal the potential impact of the topology-based modularity density to outperform the counterpart modularity functions in almost all evaluation metrics.

An Evolutionary Algorithm With Heuristic Operator for Detecting Protein Complexes in Protein Interaction Networks With Negative Controls

An Evolutionary Algorithm With Heuristic Operator for Detecting Protein Complexes in Protein Interaction Networks With Negative Controls

A computational framework for the inference of protein complex remodeling from whole-proteome measurements

Protein complexes are responsible for the enactment of most cellular functions. For the protein complex to form and function, its subunits often need to be present at defined quantitative ratios. Typically, global changes in protein complex composition are assessed with experimental approaches that tend to be time consuming. Here, we have developed a computational algorithm for the detection of altered protein complexes based on the systematic assessment of subunit ratios from quantitative proteomic measurements. We applied it to measurements from breast cancer cell lines and patient biopsies and were able to identify strong remodeling of HDAC2 epigenetic complexes in more aggressive forms of cancer. The presented algorithm is available as an R package and enables the inference of changes in protein complex states by extracting functionally relevant information from bottom-up proteomic datasets.

Biochemical and proteomic insights into sarcoplasmic reticulum Ca2+-ATPase complexes in skeletal muscles

ABSTRACT Introduction Skeletal muscles contain large numbers of high-molecular-mass protein complexes in elaborate membrane systems. Integral membrane proteins are involved in diverse cellular functions including the regulation of ion handling, membrane homeostasis, energy metabolism and force transmission. Areas covered The proteomic profiling of membrane proteins and large protein assemblies in skeletal muscles are outlined in this article. This includes a critical overview of the main biochemical separation techniques and the mass spectrometric approaches taken to study membrane proteins. As an illustrative example of an analytically challenging large protein complex, the proteomic detection and characterization of the Ca2+-ATPase of the sarcoplasmic reticulum is discussed. The biological role of this large protein complex during normal muscle functioning, in the context of fiber type diversity and in relation to mechanisms of physiological adaptations and pathophysiological abnormalities is evaluated from a proteomics perspective. Expert opinion Mass spectrometry-based muscle proteomics has decisively advanced the field of basic and applied myology. Although it is technically challenging to study membrane proteins, innovations in protein separation methodology in combination with sensitive mass spectrometry and improved systems bioinformatics has allowed the detailed proteomic detection and characterization of skeletal muscle membrane protein complexes, such as Ca2+-pump proteins of the sarcoplasmic reticulum.

CACO: A core-attachment method with cross-species functional ortholog information to detect human protein complexes.

Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes. CACO is available at https://csuligroup.com/CACO/ and https://github.com/CSUBioGroup/CACO.

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein–protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

Detection, visualization and quantification of protein complexes in human Alzheimer’s disease brains using proximity ligation assay

Examination of healthy and diseased human brain is essential to translational neuroscience. Protein–protein interactions play a pivotal role in physiological and pathological processes, but their detection is difficult, especially in aged and fixed human brain tissue. We used the in-situ proximity ligation assay (PLA) to broaden the range of molecular interactions assessable in-situ in the human neuropathology. We adapted fluorescent in-situ PLA to detect ubiquitin-modified proteins in human brains with Alzheimer’s disease (AD), including approaches for the management of autofluorescence and quantification using a high-content image analysis system. We confirmed that phosphorylated microtubule-associated protein tau (Serine202, Threonine205) aggregates were modified by ubiquitin and that phospho-tau-ubiquitin complexes were increased in hippocampal and frontal cortex regions in AD compared to non-AD brains. Overall, we refined PLA for use in human neuropathology, which has revealed a profound change in the distribution of ubiquitin in AD brain and its association with characteristic tau pathologies.

A Network Clustering Algorithm for Protein Complex Detection Fused with Power-Law Distribution Characteristic

Network clustering for mining protein complexes from protein–protein interaction (PPI) networks has emerged as a prominent research area in data mining and bioinformatics. Accurately identifying complexes plays a crucial role in comprehending cellular organization and functionality. Network characteristics are often useful in enhancing the performance of protein complex detection methods. Many protein complex detection algorithms have been proposed, primarily focusing on local micro-topological structure metrics while overlooking the potential power-law distribution characteristic of community sizes at the macro global level. The effective use of this distribution characteristic information may be beneficial for mining protein complexes. This paper proposes a network clustering algorithm for protein complex detection fused with power-law distribution characteristic. The clustering algorithm constructs a cluster generation model based on scale-free power-law distribution to generate a cluster with a dense center and relatively sparse periphery. Following the cluster generation model, a candidate cluster is obtained. From a global perspective, the number distribution of clusters of varying sizes is taken into account. If the candidate cluster aligns with the constraints defined by the power-law distribution function of community sizes, it is designated as the final cluster; otherwise, it is discarded. To assess the prediction performance of the proposed algorithm, the gold standard complex sets CYC2008 and MIPS are employed as benchmarks. The algorithm is compared to DPClus, IPCA, SEGC, Core, SR-MCL, and ELF-DPC in terms of F-measure and Accuracy on several widely used protein–protein interaction networks. The experimental results show that the algorithm can effectively detect protein complexes and is superior to other comparative algorithms. This study further enriches the connection between analyzing complex network topology features and mining network function modules, thereby significantly contributing to the improvement of protein complex detection performance.

A Special Structural Based Weighted Network Approach for the Analysis of Protein Complexes

The detection and analysis of protein complexes is essential for understanding the functional mechanism and cellular integrity. Recently, several techniques for detecting and analysing protein complexes from Protein–Protein Interaction (PPI) dataset have been developed. Most of those techniques are inefficient in terms of detecting, overlapping complexes, exclusion of attachment protein in complex core, inability to detect inherent structures of underlying complexes, have high false-positive rates and an enrichment analysis. To address these limitations, we introduce a special structural-based weighted network approach for the analysis of protein complexes based on a Weighted Edge, Core-Attachment and Local Modularity structures (WECALM). Experimental results indicate that WECALM performs relatively better than existing algorithms in terms of accuracy, computational time, and p-value. A functional enrichment analysis also shows that WECALM is able to identify a large number of biologically significant protein complexes. Overall, WECALM outperforms other approaches by striking a better balance of accuracy and efficiency in the detection of protein complexes.

An Evolutionary Algorithm with Gene Ontology-Aware Crossover Operator for Protein Complex Detection

Evolutionary algorithms (EAs), as global search methods, are proved to be more robust than their counterpart local heuristics for detecting protein complexes in protein-protein interaction (PPI) networks. Typically, the source of robustness of these EAs comes from their components and parameters. These components are solution representation, selection, crossover, and mutation. Unfortunately, almost all EA based complex detection methods suggested in the literature were designed with only canonical or traditional components. Further, topological structure of the protein network is the main information that is used in the design of almost all such components. The main contribution of this paper is to formulate a more robust EA with more biological consistency. For this purpose, a new crossover operator is suggested where biological information in terms of both gene semantic similarity and protein functional similarity is fed into its design. To reflect the heuristic roles of both semantic and functional similarities, this paper introduces two gene ontology (GO) aware crossover operators. These are direct annotation-aware and inherited annotation-aware crossover operators. The first strategy is handled with the direct gene ontology annotation of the proteins, while the second strategy is handled with the directed acyclic graph (DAG) of each gene ontology term in the gene product. To conduct our experiments, the proposed EAs with GO-aware crossover operators are compared against the state-of-the-art heuristic, canonical EAs with the traditional crossover operator, and GO-based EAs. Simulation results are evaluated in terms of recall, precision, and F measure at both complex level and protein level. The results prove that the new EA design encourages a more reliable treatment of exploration and exploitation and, thus, improves the detection ability for more accurate protein complex structures.

A partially shared joint clustering framework for detecting protein complexes from multiple state-specific signed interaction networks

Detecting protein complexes is critical for studying cellular organizations and functions. The accumulation of protein–protein interaction (PPI) data enables the identification of protein complexes computationally. Although a great number of computational methods have been proposed to identify protein complexes from PPI networks, most of them ignore the signs of PPIs that reflect the ways proteins interact (activation or inhibition). As not all PPIs imply co-complex relationships, taking into account the signs of PPIs can benefit the identification of protein complexes. Moreover, PPI networks are not static, but vary with the change of cell states or environments. However, existing methods are primarily designed for single-network clustering, and rarely consider joint clustering of multiple PPI networks. In this study, we propose a novel partially shared signed network clustering (PS-SNC) model for identifying protein complexes from multiple state-specific signed PPI networks jointly. PS-SNC can not only consider the signs of PPIs, but also identify the common and unique protein complexes in different states. Experimental results on synthetic and real datasets show that our PS-SNC model can achieve better performance than other state-of-the-art protein complex detection methods. Extensive analysis on real datasets demonstrate the effectiveness of PS-SNC in revealing novel insights about the underlying patterns of different cell lines.

Investigating the dynamics of protein-protein interactions in plants.

Both stable and transient protein interactions play an important role in the complex assemblies required for the proper functioning of living cells. Several methods have been developed to monitor protein-protein interactions in plants. However, the detection of dynamic protein complexes is very challenging, with few technologies available for this purpose. Here, we developed a new platform using the plant UBIQUITIN promoter to drive transgene expression and thereby to detect protein interactions in planta. Typically, to decide which side of the protein to link the tags, the subcellular localization of the protein fused either N-terminal or C-terminal mCitrine was firstly confirmed by using eight different specific mCherry markers. Following stable or transient protein expression in plants, the protein interaction network was detected by affinity purification mass spectrometry. These interactions were subsequently confirmed by bimolecular fluorescence complementation (BiFC), bioluminescence resonance energy transfer and co-immunoprecipitation assays. The dynamics of these interactions were monitored by Förster resonance energy transfer (FRET) and split-nano luciferase, whilst the ternary protein complex association was monitored by BiFC-FRET. Using the canonical glycolytic metabolon as an example, the interaction between these enzymes was characterized under conditions that mimic physiologically relevant energy statuses.

A Tri-Gene Ontology Migration Operator for Improving the Performance of Meta-heuristics in Complex Detection Problems

Detecting protein complexes in protein-protein interaction (PPI) networks is a challenging problem in computational biology. To uncover a PPI network into a complex structure, different meta-heuristic algorithms have been proposed in the literature. Unfortunately, many of such methods, including evolutionary algorithms (EAs), are based solely on the topological information of the network rather than on biological information. Despite the effectiveness of EAs over heuristic methods, more inherent biological properties of proteins are rarely investigated and exploited in these approaches. In this paper, we proposed an EA with a new mutation operator for complex detection problems. The proposed mutation operator is formulated under four expressions depending on the type of gene sub-ontology. To demonstrate the performance of the proposed evolutionary based complex detection algorithm, the Saccharomyces Cerevisiae (yeast) PPI network is used in the evaluation. The results reveal that the proposed algorithm achieves more accurate complex structures than the counterpart heuristic algorithms and the canonical evolutionary algorithm based on the topological-aware mutation operator.

Search for clusters among co-occurring objects and detection of protein complexes

The method of statistically based search for structures in data of different natures, previously proposed by the authors, is applied to the problem of searching for clusters of objects in sets of co-occurring objects. In bioinformatics, a similar problem arises when identifying protein complexes. A method for identifying significant interactions between proteins is proposed, based on the use of a statistical model in which the corresponding p-values are calculated. The algorithm for searching for clusters in one-dimensional data allows us to identify a cluster of small p-values located in the vicinity of zero. Next, we consider protein complexes that are formed only taking into account these significant interactions. Among the resulting complexes, as a rule, a giant component appears which in turn can be clustered. The proposed method for identifying significant deviations from the null hypothesis, based on searching for clusters of p-values in the vicinity of zero, is general.

Complex Prediction in Large PPI Networks Using Expansion and Stripe of Core Cliques.

The widespread availability and importance of large-scale protein-protein interaction (PPI) data demand a flurry of research efforts to understand the organisation of a cell and its functionality by analysing these data at the network level. In the bioinformatics and data mining fields, network clustering acquired a lot of attraction to examine a PPI network's topological and functional aspects. The clustering of PPI networks has been proven to be an excellent method for discovering functional modules, disclosing functions of unknown proteins, and other tasks in numerous research over the last decade. This research proposes a unique graph mining approach to detect protein complexes using dense neighbourhoods (highly connected regions) in an interaction graph. Our technique first finds size-3 cliques associated with each edge (protein interaction), and then these core cliques are expanded to form high-density subgraphs. Loosely connected proteins are stripped out from these subgraphs to produce a potential protein complex. Finally, the redundancy is removed based on the Jaccard coefficient. Computational results are presented on the yeast and human protein interaction dataset to highlight our proposed technique's efficiency. Predicted protein complexes of the proposed approach have a significantly higher score of similarity to those used as gold standards in the CYC-2008 and CORUM benchmark databases than other existing approaches.

Detecting protein complexes with multiple properties by an adaptive harmony search algorithm

BackgroundAccurate identification of protein complexes in protein-protein interaction (PPI) networks is crucial for understanding the principles of cellular organization. Most computational methods ignore the fact that proteins in a protein complex have a functional similarity and are co-localized and co-expressed at the same place and time, respectively. Meanwhile, the parameters of the current methods are specified by users, so these methods cannot effectively deal with different input PPI networks.ResultTo address these issues, this study proposes a new method called MP-AHSA to detect protein complexes with Multiple Properties (MP), and an Adaptation Harmony Search Algorithm is developed to optimize the parameters of the MP algorithm. First, a weighted PPI network is constructed using functional annotations, and multiple biological properties and the Markov cluster algorithm (MCL) are used to mine protein complex cores. Then, a fitness function is defined, and a protein complex forming strategy is designed to detect attachment proteins and form protein complexes. Next, a protein complex filtering strategy is formulated to filter out the protein complexes. Finally, an adaptation harmony search algorithm is developed to determine the MP algorithm’s parameters automatically.ConclusionsExperimental results show that the proposed MP-AHSA method outperforms 14 state-of-the-art methods for identifying protein complexes. Also, the functional enrichment analyses reveal that the protein complexes identified by the MP-AHSA algorithm have significant biological relevance.

Integrative Network Modeling Highlights the Crucial Roles of Rho-GDI Signaling Pathway in the Progression of non-Small Cell Lung Cancer.

Non-small cell lung cancer (NSCLC) is the most prevalent form of lung cancer and a leading cause of cancer-related deaths worldwide. Using an integrative approach, we analyzed a publicly available merged NSCLC transcriptome dataset using machine learning, protein-protein interaction (PPI) networks and bayesian modeling to pinpoint key cellular factors and pathways likely to be involved with the onset and progression of NSCLC. First, we generated multiple prediction models using various machine learning classifiers to classify NSCLC and healthy cohorts. Our models achieved prediction accuracies ranging from 0.83 to 1.0, with XGBoost emerging as the best performer. Next, using functional enrichment analysis (and gene co-expression network analysis with WGCNA) of the machine learning feature-selected genes, we determined that genes involved in Rho GTPase signaling that modulate actin stability and cytoskeleton were likely to be crucial in NSCLC. We further assembled a PPI network for the feature-selected genes that was partitioned using Markov clustering to detect protein complexes functionally relevant to NSCLC. Finally, we modeled the perturbations in RhoGDI signaling using a bayesian network; our simulations suggest that aberrations in ARHGEF19 and/or RAC2 gene activities contributed to impaired MAPK signaling and disrupted actin and cytoskeleton organization and were arguably key contributors to the onset of tumorigenesis in NSCLC. We hypothesize that targeted measures to restore aberrant ARHGEF19 and/or RAC2 functions could conceivably rescue the cancerous phenotype in NSCLC. Our findings offer promising avenues for early predictive biomarker discovery, targeted therapeutic intervention and improved clinical outcomes in NSCLC.

Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling.

Recent development of deep-learning methods has led to a breakthrough in the prediction accuracy of 3D protein structures. Extending these methods to protein pairs is expected to allow large-scale detection of protein-protein interactions (PPIs) and modeling protein complexes at the proteome level. We applied RoseTTAFold and AlphaFold, two of the latest deep-learning methods for structure predictions, to analyze coevolution of human proteins residing in mitochondria, an organelle of vital importance in many cellular processes including energy production, metabolism, cell death and antiviral response. Variations in mitochondrial proteins have been linked to a plethora of human diseases and genetic conditions. RoseTTAFold, with high computational speed, was used to predict the coevolution of about 95% of mitochondrial protein pairs. Top-ranked pairs were further subject to modeling of the complex structures by AlphaFold, which also produced contact probability with high precision and in many cases consistent with RoseTTAFold. Most top-ranked pairs with high contact probability were supported by known PPIs and/or similarities to experimental structural complexes. For high-scoring pairs without experimental complex structures, our coevolution analyses and structural models shed light on the details of their interfaces, including CHCHD4-AIFM1, MTERF3-TRUB2, FMC1-ATPAF2 and ECSIT-NDUFAF1. We also identified novel PPIs (PYURF-NDUFAF5, LYRM1-MTRF1L and COA8-COX10) for several proteins without experimentally characterized interaction partners, leading to predictions of their molecular functions and the biological processes they are involved in. Data of mitochondrial proteins and their interactions are available at: http://conglab.swmed.edu/mitochondria. Supplementary data are available at Bioinformatics online.