Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

Mazloom, Amin R.; Dannenfelser, Ruth; Clark, Neil R.; Grigoryan, Arsen; Linder, Kathryn M.; Cardozo, Timothy; Bond, Julia C.; Boran, Aislyn; Iyengar, Ravi; Malovannaya, Anna; Lanz, Rainer B.; Ma’ayan, Avi

doi:10.1371/journal.pcbi.1002319

Cited by 17 publications

(16 citation statements)

References 52 publications

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“…However, correlation did not consistently reveal the directly bound protein pairs that other experiments such as yeast two-hybrid [8, 9] and chemical crosslinking [10–14] can reveal across large portions of the proteome. Other computational approaches have been proposed to identify direct contacts by analyzing co-occurrence of proteins in mass spectrometry experiments but they have only been applied to AP-MS datasets [15]. …”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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

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Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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“…To exemplify that the performance of CAPPIC is consistent for different taxonomic species, we also applied it to two human networks: Y2H-human (Additional file 1: Table S1) and Mazloom [53]. Figure 5 shows the corresponding ROC plots summarizing the performance of CAPPIC and of the reference methods (analogous to Figure 2), as well as the GO semantic similarity as a function of the CAPPIC score (analogous to Figure 4) for these networks.…”

Section: Resultsmentioning

confidence: 99%

“…For example, CAPPIC achieved 90% AUC on the Mazloom network and 62% AUC on the much sparser yeast-two-hybrid network, outperforming the reference methods in both cases (Figure 5). In the case of the Mazloom network, we also measured the agreement between CAPPIC scores and interaction ranks that were based on evidence from 3,290 co-immunoprecipitation experiments [53]. The CAPPIC scores were calculated independently of the ranks or the confidence values assigned in the original study.…”

Section: Resultsmentioning

confidence: 99%

“…[5-8] (Additional file 1: Table S1) ( Y2H-human ). The second network corresponded to the top 5% interactions from a probabilistic binary data set generated by Mazloom et al [53] from mass spectrometry-based analysis of 3,290 immuno-precipitation experiments [54] ( Mazloom ). The properties of the two human networks are summarized in Additional file 2: Table S2.…”

Section: Introductionmentioning

confidence: 99%

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Cluster-based assessment of protein-protein interaction confidence

et al. 2012

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BackgroundProtein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity.ResultsWe propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network’s inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring.ConclusionsOn the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at http://intscore.molgen.mpg.de.

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“…The NURSA IP-MS dataset is essentially a gene-set library where each term is a pull-down experiment defined by the bait protein and the antibody, and the genes in each set are the co-precipitated proteins identified by mass spectrometry. Each IP-MS experiment provides a snapshot of the entire protein interactome and binary interactions can be inferred from such data [76, 77]. Databases that consolidate knowledge about protein complexes such as CORUM [78] can also be converted to gene-set libraries or used for predicting binary protein interactions.…”

Section: High-content Datasets and Resourcesmentioning

confidence: 99%

Publisher's Note: Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction

2015

Computational Biology and Chemistry

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With advances in genomics, transcriptomics, metabolomics and proteomics, and more expansive electronic clinical record monitoring, as well as advances in computation, we have entered the Big Data era in biomedical research. Data gathering is growing rapidly while only a small fraction of this data is converted to useful knowledge or reused in future studies. To improve this, an important concept that is often overlooked is data abstraction. To fuse and reuse biomedical datasets from diverse resources, data abstraction is frequently required. Here we summarize some of the major Big Data biomedical research resources for genomics, proteomics and phenotype data, collected from mammalian cells, tissues and organisms. We then suggest simple data abstraction methods for fusing this diverse but related data. Finally, we demonstrate examples of the potential utility of such data integration efforts, while warning about the inherit biases that exist within such data.

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Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

Cited by 17 publications

References 52 publications

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

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

Cluster-based assessment of protein-protein interaction confidence

Publisher's Note: Abstraction for data integration: Fusing mammalian molecular, cellular and phenotype big datasets for better knowledge extraction

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