Protein-protein Interaction Datasets Research Articles

Identification of protein complexes from multi-relationship protein interaction networks.

BackgroundProtein complexes play an important role in biological processes. Recent developments in experiments have resulted in the publication of many high-quality, large-scale protein-protein interaction (PPI) datasets, which provide abundant data for computational approaches to the prediction of protein complexes. However, the precision of protein complex prediction still needs to be improved due to the incompletion and noise in PPI networks.ResultsThere exist complex and diverse relationships among proteins after integrating multiple sources of biological information. Considering that the influences of different types of interactions are not the same weight for protein complex prediction, we construct a multi-relationship protein interaction network (MPIN) by integrating PPI network topology with gene ontology annotation information. Then, we design a novel algorithm named MINE (identifying protein complexes based on Multi-relationship protein Interaction NEtwork) to predict protein complexes with high cohesion and low coupling from MPIN.ConclusionsThe experiments on yeast data show that MINE outperforms the current methods in terms of both accuracy and statistical significance.

Predicting overlapping protein complexes from weighted protein interaction graphs by gradually expanding dense neighborhoods.

Proteins are vital biological molecules driving many fundamental cellular processes. They rarely act alone, but form interacting groups called protein complexes. The study of protein complexes is a key goal in systems biology. Recently, large protein-protein interaction (PPI) datasets have been published and a plethora of computational methods that provide new ideas for the prediction of protein complexes have been implemented. However, most of the methods suffer from two major limitations: First, they do not account for proteins participating in multiple functions and second, they are unable to handle weighted PPI graphs. Moreover, the problem remains open as existing algorithms and tools are insufficient in terms of predictive metrics. In the present paper, we propose gradually expanding neighborhoods with adjustment (GENA), a new algorithm that gradually expands neighborhoods in a graph starting from highly informative "seed" nodes. GENA considers proteins as multifunctional molecules allowing them to participate in more than one protein complex. In addition, GENA accepts weighted PPI graphs by using a weighted evaluation function for each cluster. In experiments with datasets from Saccharomyces cerevisiae and human, GENA outperformed Markov clustering, restricted neighborhood search and clustering with overlapping neighborhood expansion, three state-of-the-art methods for computationally predicting protein complexes. Seven PPI networks and seven evaluation datasets were used in total. GENA outperformed existing methods in 16 out of 18 experiments achieving an average improvement of 5.5% when the maximum matching ratio metric was used. Our method was able to discover functionally homogeneous protein clusters and uncover important network modules in a Parkinson expression dataset. When used on the human networks, around 47% of the detected clusters were enriched in gene ontology (GO) terms with depth higher than five in the GO hierarchy. In the present manuscript, we introduce a new method for the computational prediction of protein complexes by making the realistic assumption that proteins participate in multiple protein complexes and cellular functions. Our method can detect accurate and functionally homogeneous clusters.

Quantitative Tagless Copurification: A Method to Validate and Identify Protein-Protein Interactions

Identifying protein-protein interactions (PPIs) at an acceptable false discovery rate (FDR) is challenging. Previously we identified several hundred PPIs from affinity purification - mass spectrometry (AP-MS) data for the bacteria Escherichia coli and Desulfovibrio vulgaris. These two interactomes have lower FDRs than any of the nine interactomes proposed previously for bacteria and are more enriched in PPIs validated by other data than the nine earlier interactomes. To more thoroughly determine the accuracy of ours or other interactomes and to discover further PPIs de novo, here we present a quantitative tagless method that employs iTRAQ MS to measure the copurification of endogenous proteins through orthogonal chromatography steps. 5273 fractions from a four-step fractionation of a D. vulgaris protein extract were assayed, resulting in the detection of 1242 proteins. Protein partners from our D. vulgaris and E. coli AP-MS interactomes copurify as frequently as pairs belonging to three benchmark data sets of well-characterized PPIs. In contrast, the protein pairs from the nine other bacterial interactomes copurify two- to 20-fold less often. We also identify 200 high confidence D. vulgaris PPIs based on tagless copurification and colocalization in the genome. These PPIs are as strongly validated by other data as our AP-MS interactomes and overlap with our AP-MS interactome for D.vulgaris within 3% of expectation, once FDRs and false negative rates are taken into account. Finally, we reanalyzed data from two quantitative tagless screens of human cell extracts. We estimate that the novel PPIs reported in these studies have an FDR of at least 85% and find that less than 7% of the novel PPIs identified in each screen overlap. Our results establish that a quantitative tagless method can be used to validate and identify PPIs, but that such data must be analyzed carefully to minimize the FDR.

Construction of dynamic probabilistic protein interaction networks for protein complex identification.

BackgroundRecently, high-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data which can construct large complex PPI networks for numerous organisms. System biology attempts to understand cellular organization and function by analyzing these PPI networks. However, most studies still focus on static PPI networks which neglect the dynamic information of PPI.ResultsThe gene expression data under different time points and conditions can reveal the dynamic information of proteins. In this study, we used an active probability-based method to distinguish the active level of proteins at different active time points. We constructed dynamic probabilistic protein networks (DPPN) to integrate dynamic information of protein into static PPI networks. Based on DPPN, we subsequently proposed a novel method to identify protein complexes, which could effectively exploit topological structure as well as dynamic information of DPPN. We used three different yeast PPI datasets and gene expression data to construct three DPPNs. When applied to three DPPNs, many well-characterized protein complexes were accurately identified by this method.ConclusionThe shift from static PPI networks to dynamic PPI networks is essential to accurately identify protein complex. This method not only can be applied to identify protein complex, but also establish a framework to integrate dynamic information into static networks for other applications, such as pathway analysis.

MoCha: Molecular Characterization of Unknown Pathways.

Automated methods for the reverse-engineering of complex regulatory networks are paving the way for the inference of mechanistic comprehensive models directly from experimental data. These novel methods can infer not only the relations and parameters of the known molecules defined in their input datasets, but also unknown components and pathways identified as necessary by the automated algorithms. Identifying the molecular nature of these unknown components is a crucial step for making testable predictions and experimentally validating the models, yet no specific and efficient tools exist to aid in this process. To this end, we present here MoCha (Molecular Characterization), a tool optimized for the search of unknown proteins and their pathways from a given set of known interacting proteins. MoCha uses the comprehensive dataset of protein-protein interactions provided by the STRING database, which currently includes more than a billion interactions from over 2,000 organisms. MoCha is highly optimized, performing typical searches within seconds. We demonstrate the use of MoCha with the characterization of unknown components from reverse-engineered models from the literature. MoCha is useful for working on network models by hand or as a downstream step of a model inference engine workflow and represents a valuable and efficient tool for the characterization of unknown pathways using known data from thousands of organisms. MoCha and its source code are freely available online under the GPLv3 license.

A New Method for Predicting Protein Functions From Dynamic Weighted Interactome Networks.

Automated annotation of protein function is challenging. As the number of sequenced genomes rapidly grows, the overwhelming majority of proteins can only be annotated computationally. Under new conditions or stimuli, not only the number and location of proteins would be changed, but also their interactions. This dynamic feature of protein interactions, however, was not considered in the existing function prediction algorithms. Taking the dynamic nature of protein interactions into consideration, we construct a dynamic weighted interactome network (DWIN) by integrating protein-protein interaction (PPI) network and time course gene expression data, as well as proteins' domain information and protein complex information. Then, we propose a new prediction approach that predicts protein functions from the constructed dynamic weighted interactome network. For an unknown protein, the proposed method visits dynamic networks at different time points and scores functions derived from all neighbors. Finally, the method selects top N functions from these ranked candidate functions to annotate the testing protein. Experiments on PPI datasets were conducted to evaluate the effectiveness of the proposed approach in predicting unknown protein functions. The evaluation results demonstrated that the proposed method outperforms other competing methods.

Bayesian module identification from multiple noisy networks.

Background and motivationsModule identification has been studied extensively in order to gain deeper understanding of complex systems, such as social networks as well as biological networks. Modules are often defined as groups of vertices in these networks that are topologically cohesive with similar interaction patterns with the rest of the vertices. Most of the existing module identification algorithms assume that the given networks are faithfully measured without errors. However, in many real-world applications, for example, when analyzing protein-protein interaction networks from high-throughput profiling techniques, there is significant noise with both false positive and missing links between vertices. In this paper, we propose a new model for more robust module identification by taking advantage of multiple observed networks with significant noise so that signals in multiple networks can be strengthened and help improve the solution quality by combining information from various sources.MethodsWe adopt a hierarchical Bayesian model to integrate multiple noisy snapshots that capture the underlying modular structure of the networks under study. By introducing a latent root assignment matrix and its relations to instantaneous module assignments in all the observed networks to capture the underlying modular structure and combine information across multiple networks, an efficient variational Bayes algorithm can be derived to accurately and robustly identify the underlying modules from multiple noisy networks.ResultsExperiments on synthetic and protein-protein interaction data sets show that our proposed model enhances both the accuracy and resolution in detecting cohesive modules, and it is less vulnerable to noise in the observed data. In addition, it shows higher power in predicting missing edges compared to individual-network methods.

Using contrast patterns between true complexes and random subgraphs in PPI networks to predict unknown protein complexes.

Most protein complex detection methods utilize unsupervised techniques to cluster densely connected nodes in a protein-protein interaction (PPI) network, in spite of the fact that many true complexes are not dense subgraphs. Supervised methods have been proposed recently, but they do not answer why a group of proteins are predicted as a complex, and they have not investigated how to detect new complexes of one species by training the model on the PPI data of another species. We propose a novel supervised method to address these issues. The key idea is to discover emerging patterns (EPs), a type of contrast pattern, which can clearly distinguish true complexes from random subgraphs in a PPI network. An integrative score of EPs is defined to measure how likely a subgraph of proteins can form a complex. New complexes thus can grow from our seed proteins by iteratively updating this score. The performance of our method is tested on eight benchmark PPI datasets and compared with seven unsupervised methods, two supervised and one semi-supervised methods under five standards to assess the quality of the predicted complexes. The results show that in most cases our method achieved a better performance, sometimes significantly.

Detection of Interactions between Proteins through Rotation Forest and Local Phase Quantization Descriptors.

Protein-Protein Interactions (PPIs) play a vital role in most cellular processes. Although many efforts have been devoted to detecting protein interactions by high-throughput experiments, these methods are obviously expensive and tedious. Targeting these inevitable disadvantages, this study develops a novel computational method to predict PPIs using information on protein sequences, which is highly efficient and accurate. The improvement mainly comes from the use of the Rotation Forest (RF) classifier and the Local Phase Quantization (LPQ) descriptor from the Physicochemical Property Response (PR) Matrix of protein amino acids. When performed on three PPI datasets including Saccharomyces cerevisiae, Homo sapiens, and Helicobacter pylori, we obtained good results of average accuracies of 93.8%, 97.96%, and 89.47%, which are much better than in previous studies. Extensive validations have also been explored to evaluate the performance of the Rotation Forest ensemble classifier with the state-of-the-art Support Vector Machine classifier. These promising results indicate that the proposed method might play a complementary role for future proteomics research.

A bioinformatic survey of RNA-binding proteins in Plasmodium.

BackgroundThe malaria parasites in the genus Plasmodium have a very complicated life cycle involving an invertebrate vector and a vertebrate host. RNA-binding proteins (RBPs) are critical factors involved in every aspect of the development of these parasites. However, very few RBPs have been functionally characterized to date in the human parasite Plasmodium falciparum.MethodsUsing different bioinformatic methods and tools we searched P. falciparum genome to list and annotate RBPs. A representative 3D models for each of the RBD domain identified in P. falciparum was created using I-TESSAR and SWISS-MODEL. Microarray and RNAseq data analysis pertaining PfRBPs was performed using MeV software. Finally, Cytoscape was used to create protein-protein interaction network for CITH-Dozi and Caf1-CCR4-Not complexes.ResultsWe report the identification of 189 putative RBP genes belonging to 13 different families in Plasmodium, which comprise 3.5 % of all annotated genes. Almost 90 % (169/189) of these genes belong to six prominent RBP classes, namely RNA recognition motifs, DEAD/H-box RNA helicases, K homology, Zinc finger, Puf and Alba gene families. Interestingly, almost all of the identified RNA-binding helicases and KH genes have cognate homologs in model species, suggesting their evolutionary conservation. Exploration of the existing P. falciparum blood-stage transcriptomes revealed that most RBPs have peak mRNA expression levels early during the intraerythrocytic development cycle, which taper off in later stages. Nearly 27 % of RBPs have elevated expression in gametocytes, while 47 and 24 % have elevated mRNA expression in ookinete and asexual stages. Comparative interactome analyses using human and Plasmodium protein-protein interaction datasets suggest extensive conservation of the PfCITH/PfDOZI and PfCaf1-CCR4-NOT complexes.ConclusionsThe Plasmodium parasites possess a large number of putative RBPs belonging to most of RBP families identified so far, suggesting the presence of extensive post-transcriptional regulation in these parasites. Taken together, in silico identification of these putative RBPs provides a foundation for future functional studies aimed at defining a unique network of post-transcriptional regulation in P. falciparum.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-015-2092-1) contains supplementary material, which is available to authorized users.

Protein complex detection in PPI networks based on data integration and supervised learning method.

BackgroundRevealing protein complexes are important for understanding principles of cellular organization and function. High-throughput experimental techniques have produced a large amount of protein interactions, which makes it possible to predict protein complexes from protein-protein interaction (PPI) networks. However, the small amount of known physical interactions may limit protein complex detection.MethodsThe new PPI networks are constructed by integrating PPI datasets with the large and readily available PPI data from biomedical literature, and then the less reliable PPI between two proteins are filtered out based on semantic similarity and topological similarity of the two proteins. Finally, the supervised learning protein complex detection (SLPC), which can make full use of the information of available known complexes, is applied to detect protein complex on the new PPI networks.ResultsThe experimental results of SLPC on two different categories yeast PPI networks demonstrate effectiveness of the approach: compared with the original PPI networks, the best average improvements of 4.76, 6.81 and 15.75 percentage units in the F-score, accuracy and maximum matching ratio (MMR) are achieved respectively; compared with the denoising PPI networks, the best average improvements of 3.91, 4.61 and 12.10 percentage units in the F-score, accuracy and MMR are achieved respectively; compared with ClusterONE, the start-of the-art complex detection method, on the denoising extended PPI networks, the average improvements of 26.02 and 22.40 percentage units in the F-score and MMR are achieved respectively.ConclusionsThe experimental results show that the performances of SLPC have a large improvement through integration of new receivable PPI data from biomedical literature into original PPI networks and denoising PPI networks. In addition, our protein complexes detection method can achieve better performance than ClusterONE.

Revisiting topological properties and models of protein-protein interaction networks from the perspective of dataset evolution.

Protein-protein interaction (PPI) networks are crucial for organisms. Many research efforts have thus been devoted to the study on the topological properties and models of PPI networks. However, existing studies did not always report consistent results on the topological properties of PPI networks. Although a number of PPI network models have been introduced, yet in the literature there is no convincing conclusion on which model is best for describing PPI networks. This situation is primarily caused by the incompleteness of current PPI datasets. To solve this problem, in this study, the authors propose to revisit the topological properties and models of PPI networks from the perspective of PPI dataset evolution. Concretely, they used 12 PPI datasets of Arabidopsis thaliana and 10 PPI datasets of Saccharomyces cerevisiae from different Biological General Repository for Interaction Datasets (BioGRID) database versions, and compared the topological properties of these datasets and the fitting capabilities of five typical PPI network models over these datasets.

Abstract SY19-03: Rewiring of signaling associated protein complexes in cancer

Abstract Attacking aberrantly activated kinases has now become a standard of care for certain cancers, such as subtypes of lung cancer driven by genetically altered receptor tyrosine kinases (RTK, i.e., EGFR, EML4-ALK). While genomic alterations in RTK identify patients likely to respond, the underlying molecular mechanisms that drive the magnitude of response as well as the duration of response remain unclear. In addition, acquired and intrinsic therapeutic resistance remains a problem precluding cure. While genomic strategies can be helpful in identifying tumors driven by RTK and susceptive to RTK inhibitors, increasing evidence suggest a role for multiple RTK activation on a single cancer cell. Importantly, RTK bypass can occur through stromal mechanisms, such as hepatocyte growth factor (HGF) driven MET activation, which cannot be assessed through genomic technology. A major challenge in the field is to accurately identify bypass RTK in individual tumors accounting for both tumor-cell autonomous features (genome) as well as tumor exogenous features (tumor stroma). Activation of RTK through diverse mechanisms, including gene mutation, gene amplification, or overproduction of activating growth factor ligands, leads to assembly of protein complexes on RTK that facilitate downstream signal transduction. Exploiting this fact, we have developed proximity ligation assays (PLA) to annotate protein complexes reflecting an activated EGFR, a key RTK in lung cancer and other cancers. We show that PLA can be developed starting with mass spectrometry based protein-protein interaction datasets to characterize in situ EGFR complexes reflecting active signaling in cancer tissues. We have developed one such assay reflecting EGFR in complex with its adaptor protein GRB2. We show these assays reflect EGFR kinase activity both in vitro using cancer cell lines and in vivo using mouse xenograft models of lung cancer. Importantly, these assays perform well in formalin fixed paraffin embedded (FFPE) tissues enabling wide spread utility in typical hospital tumor samples. We demonstrate feasibility and utility in characterizing EGFR:GRB2 complexes in nearly 300 patient derived xenograft models of cancer. Further, our data indicate good concordance of high PLA signals across three independent cohorts of lung cancer patients numbering 350 unique cases. Our human tumor data shows high PLA signal in tumors with activating EGFR mutation as expected but we can further identify tumors with high PLA signal that lack EGFR mutation or with KRAS mutation, thus representing tumor stromal activation of EGFR. In both the PDX and patient cohorts, high EGFR:GRB2 signal by PLA is associated with responsiveness to anti-EGFR therapy. Ongoing work examining MET, ALK, and FGFR protein complexes will be discussed. This work opens up the human protein interactome as a new class of molecular markers for disease in a more practical manner. Proteins, encoded by DNA, do not work in isolation but instead function as part of multi-protein complexes that drive both normal and disease physiology. Therefore, annotation of such complexes may allow a new view toward disease characterization. Importantly, while we demonstrate the utility of this approach in lung cancer, receptor tyrosine kinase signaling and tyrosine kinase inhibitor therapeutics, our approach described here could have utility across a wide spectrum of both signaling-associated complexes and different types of disease. Citation Format: Eric B. Haura. Rewiring of signaling associated protein complexes in cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr SY19-03. doi:10.1158/1538-7445.AM2015-SY19-03

MOEPGA: A novel method to detect protein complexes in yeast protein–protein interaction networks based on MultiObjective Evolutionary Programming Genetic Algorithm

The identification of protein complexes in protein–protein interaction (PPI) networks has greatly advanced our understanding of biological organisms. Existing computational methods to detect protein complexes are usually based on specific network topological properties of PPI networks. However, due to the inherent complexity of the network structures, the identification of protein complexes may not be fully addressed by using single network topological property. In this study, we propose a novel MultiObjective Evolutionary Programming Genetic Algorithm (MOEPGA) which integrates multiple network topological features to detect biologically meaningful protein complexes. Our approach first systematically analyzes the multiobjective problem in terms of identifying protein complexes from PPI networks, and then constructs the objective function of the iterative algorithm based on three common topological properties of protein complexes from the benchmark dataset, finally we describe our algorithm, which mainly consists of three steps, population initialization, subgraph mutation and subgraph selection operation. To show the utility of our method, we compared MOEPGA with several state-of-the-art algorithms on two yeast PPI datasets. The experiment results demonstrate that the proposed method can not only find more protein complexes but also achieve higher accuracy in terms of fscore. Moreover, our approach can cover a certain number of proteins in the input PPI network in terms of the normalized clustering score. Taken together, our method can serve as a powerful framework to detect protein complexes in yeast PPI networks, thereby facilitating the identification of the underlying biological functions.

DAPPER: a data-mining resource for protein-protein interactions.

BackgroundThe identification of interaction networks between proteins and complexes holds the promise of offering novel insights into the molecular mechanisms that regulate many biological processes. With increasing volumes of such datasets, especially in model organisms such as Drosophila melanogaster, there exists a pressing need for specialised tools, which can seamlessly collect, integrate and analyse these data. Here we describe a database coupled with a mining tool for protein-protein interactions (DAPPER), developed as a rich resource for studying multi-protein complexes in Drosophila melanogaster.ResultsThis proteomics database is compiled through mass spectrometric analyses of many protein complexes affinity purified from Drosophila tissues and cultured cells. The web access to DAPPER is provided via an accelerated version of BioMart software enabling data-mining through customised querying and output formats. The protein-protein interaction dataset is annotated with FlyBase identifiers, and further linked to the Ensembl database using BioMart’s data-federation model, thereby enabling complex multi-dataset queries. DAPPER is open source, with all its contents and source code are freely available.ConclusionsDAPPER offers an easy-to-navigate and extensible platform for real-time integration of diverse resources containing new and existing protein-protein interaction datasets of Drosophila melanogaster.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-015-0063-3) contains supplementary material, which is available to authorized users.

A scalable method for molecular network reconstruction identifies properties of targets and mutations in acute myeloid leukemia.

A key aim of systems biology is the reconstruction of molecular networks. We do not yet, however, have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms. Network reconstruction methods should also be scalable in the sense of allowing scientists from different backgrounds to efficiently integrate additional data. We present a network model of acute myeloid leukemia (AML). In the current version (AML 2.1), we have used gene expression data (both microarray and RNA-seq) from 5 different studies comprising a total of 771 AML samples and a protein-protein interactions dataset. Our scalable network reconstruction method is in part based on the well-known property of gene expression correlation among interacting molecules. The difficulty of distinguishing between direct and indirect interactions is addressed by optimizing the coefficient of variation of gene expression, using a validated gold-standard dataset of direct interactions. Computational time is much reduced compared to other network reconstruction methods. A key feature is the study of the reproducibility of interactions found in independent clinical datasets. An analysis of the most significant clusters, and of the network properties (intraset efficiency, degree, betweenness centrality, and PageRank) of common AML mutations demonstrated the biological significance of the network. A statistical analysis of the response of blast cells from 11 AML patients to a library of kinase inhibitors provided an experimental validation of the network. A combination of network and experimental data identified CDK1, CDK2, CDK4, and CDK6 and other kinases as potential therapeutic targets in AML.

Multi-species network inference improves gene regulatory network reconstruction for early embryonic development in Drosophila.

Gene regulatory network inference uses genome-wide transcriptome measurements in response to genetic, environmental, or dynamic perturbations to predict causal regulatory influences between genes. We hypothesized that evolution also acts as a suitable network perturbation and that integration of data from multiple closely related species can lead to improved reconstruction of gene regulatory networks. To test this hypothesis, we predicted networks from temporal gene expression data for 3,610 genes measured during early embryonic development in six Drosophila species and compared predicted networks to gold standard networks of ChIP-chip and ChIP-seq interactions for developmental transcription factors in five species. We found that (i) the performance of single-species networks was independent of the species where the gold standard was measured; (ii) differences between predicted networks reflected the known phylogeny and differences in biology between the species; (iii) an integrative consensus network that minimized the total number of edge gains and losses with respect to all single-species networks performed better than any individual network. Our results show that in an evolutionarily conserved system, integration of data from comparable experiments in multiple species improves the inference of gene regulatory networks. They provide a basis for future studies on the numerous multispecies gene expression datasets for other biological processes available in the literature.

Clustering PPI data by combining FA and SHC method.

Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value.

Integrating experimental and literature protein-protein interaction data for protein complex prediction.

BackgroundAccurate determination of protein complexes is crucial for understanding cellular organization and function. High-throughput experimental techniques have generated a large amount of protein-protein interaction (PPI) data, allowing prediction of protein complexes from PPI networks. However, the high-throughput data often includes false positives and false negatives, making accurate prediction of protein complexes difficult.MethodThe biomedical literature contains large quantities of PPI data that, along with high-throughput experimental PPI data, are valuable for protein complex prediction. In this study, we employ a natural language processing technique to extract PPI data from the biomedical literature. This data is subsequently integrated with high-throughput PPI and gene ontology data by constructing attributed PPI networks, and a novel method for predicting protein complexes from the attributed PPI networks is proposed. This method allows calculation of the relative contribution of high-throughput and biomedical literature PPI data.ResultsMany well-characterized protein complexes are accurately predicted by this method when apply to two different yeast PPI datasets. The results show that (i) biomedical literature PPI data can effectively improve the performance of protein complex prediction; (ii) our method makes good use of high-throughput and biomedical literature PPI data along with gene ontology data to achieve state-of-the-art protein complex prediction capabilities.

A web-tool for visualizing quantitative protein-protein interaction data.

Quantitative interaction proteomics data can be a challenge to efficiently analyze and subsequently present to an audience in a simple and easy to understand format that still conveys sufficient levels of information. Here we present freely accessible and open-source web tools for displaying multiple parameters from quantitative protein-protein interaction data sets in a visually intuitive format. Given a set of "bait" proteins with detected "prey" interactions, dot plots can be generated to display absolute spectral counts for the preys, relative spectral counts between baits and confidence levels for the interactions (e.g. as determined by SAINTexpress). Additional tools are available for displaying fold change results between numerous baits with their associated confidence level (e.g. resulting from intensity measurements) and pairwise bait analyses displaying spectral counts, confidence score and fold change differences in a scatter plot format. These tools make it easy for the user to identify important interaction changes, interpret their data, and present this information to others in an intuitive way.