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Kemmer, Danielle; Huang, Yong; Shah, Sohrab P.; Lim, Jonathan; Brumm, Jochen; Yuen, Macaire Man Saint; Ling, John; Xu, Tao; Wasserman, Wyeth W.; Ouellette, B. F. Francis

doi:10.1186/gb-2005-6-12-r106

Cited by 21 publications

(5 citation statements)

References 58 publications

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“…Different computational methods have been proposed for inferring protein interactions from sequence alignments of orthologous proteins, assuming linkage between protein structure and function [9, 35, 36]. We thus speculated that the AHC dendrograms could correlate to E6 and E7 phylogenetic trees.…”

Section: Inter-genotypic Comparison Of E6 and E7 Interaction Profilesmentioning

confidence: 99%

Comparative analysis of virus–host interactomes with a mammalian high-throughput protein complementation assay based on Gaussia princeps luciferase

Neveu¹,

Cassonnet²,

Vidalain³

et al. 2012

Methods

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Comparative interactomics is a strategy for inferring potential interactions among orthologous proteins or “iginterologs”. Herein we focus, in contrast to standard homology-based inference, on the divergence of protein interaction profiles among closely related organisms, showing that the approach can correlate specific traits to phenotypic differences. As a model, this new comparative interactomic approach was applied at a large scale to human papillomaviruses (HPVs) proteins. The oncogenic potential of HPVs is mainly determined by the E6 and E7 early proteins. We have mapped and overlapped the virus-host protein interaction networks of E6 and E7 proteins from 11 distinct HPV genotypes, selected for their different tropisms and pathologies. We generated robust and comprehensive datasets by combining two orthogonal protein interation assays: yeast two-hybrid (Y2H), and our recently described “High-Throughput Gaussia princeps Protein Complementation Assay” (HT-GPCA). HT-GPCA detects protein interaction by measuring the interaction-mediated reconstitution of activity of a split Gaussia princeps luciferase. Hierarchical clustering of interaction profiles recapitulated HPV phylogeny and was used to correlate specific virus-host interaction profiles with pathological traits, reflecting the distinct carcinogenic potentials of different HPVs. This comparative interactomics constitutes a reliable and powerful strategy to decipher molecular relationships in virtually any combination of microorganism-host interactions.

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Section: Inter-genotypic Comparison Of E6 and E7 Interaction Profilesmentioning

confidence: 99%

Comparative analysis of virus–host interactomes with a mammalian high-throughput protein complementation assay based on Gaussia princeps luciferase

Neveu¹,

Cassonnet²,

Vidalain³

et al. 2012

Methods

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“… Interologs are two pairs of protein interactions that fulfill the following conditions: (A interacts with B) + (A is similar to A′) + (B is similar to B′) → (A′ interacts with B′). All quadruples (A, B, A′, B′) for which this relation is true are referred to as interologs [ 37 , 79 ]. To illustrate our analysis, we have to extend this simple relation.…”

Section: Introductionmentioning

confidence: 99%

Protein–Protein Interactions More Conserved within Species than across Species

2006

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Experimental high-throughput studies of protein–protein interactions are beginning to provide enough data for comprehensive computational studies. Today, about ten large data sets, each with thousands of interacting pairs, coarsely sample the interactions in fly, human, worm, and yeast. Another about 55,000 pairs of interacting proteins have been identified by more careful, detailed biochemical experiments. Most interactions are experimentally observed in prokaryotes and simple eukaryotes; very few interactions are observed in higher eukaryotes such as mammals. It is commonly assumed that pathways in mammals can be inferred through homology to model organisms, e.g. the experimental observation that two yeast proteins interact is transferred to infer that the two corresponding proteins in human also interact. Two pairs for which the interaction is conserved are often described as interologs. The goal of this investigation was a large-scale comprehensive analysis of such inferences, i.e. of the evolutionary conservation of interologs. Here, we introduced a novel score for measuring the overlap between protein–protein interaction data sets. This measure appeared to reflect the overall quality of the data and was the basis for our two surprising results from our large-scale analysis. Firstly, homology-based inferences of physical protein–protein interactions appeared far less successful than expected. In fact, such inferences were accurate only for extremely high levels of sequence similarity. Secondly, and most surprisingly, the identification of interacting partners through sequence similarity was significantly more reliable for protein pairs within the same organism than for pairs between species. Our analysis underlined that the discrepancies between different datasets are large, even when using the same type of experiment on the same organism. This reality considerably constrains the power of homology-based transfer of interactions. In particular, the experimental probing of interactions in distant model organisms has to be undertaken with some caution. More comprehensive images of protein–protein networks will require the combination of many high-throughput methods, including in silico inferences and predictions. http://www.rostlab.org/results/2006/ppi_homology/

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“…22 It is well established that many of the protein-protein interactions are confirmed via homology. 23 Homology information was obtained from Hitdb (Homologous Interactions Database), which provides high confidence homologous interactions that are experimentally determined from IntAct, BioGRID, and HPRD by PSI-Blast 18 (http://hintdb.hgc.jp/hint/). We considered 92,734 human homologous protein pairs.…”

Section: Methodsmentioning

confidence: 99%

Identification of Functional Modules by Integration of Multiple Data Sources Using a Bayesian Network Classifier

Wang

Zuo²,

Liu³

et al. 2014

Circ Cardiovasc Genet

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Background Prediction of functional modules is indispensable for detecting protein deregulation in human complex diseases such as cancer. Bayesian network (BN) is one of the most commonly used models to integrate heterogeneous data from multiple sources such as protein domain, interactome, functional annotation, genome-wide gene expression, and the literature. Methods and Results In this paper, we present a BN classifier that is customized to: 1) increase the ability to integrate diverse information from different sources, 2) effectively predict protein-protein interactions, 3) infer aberrant networks with scale-free and small world properties, and 4) group molecules into functional modules or pathways based on the primary function and biological features. Application of this model on discovering protein biomarkers of hepatocelluar carcinoma (HCC) leads to the identification of functional modules that provide insights into the mechanism of the development and progression of HCC. These functional modules include cell cycle deregulation, increased angiogenesis (e.g., vascular endothelial growth factor, blood vessel morphogenesis), oxidative metabolic alterations, and aberrant activation of signaling pathways involved in cellular proliferation, survival, and differentiation. Conclusion The discoveries and conclusions derived from our customized BN classifier are consistent with previously published results. The proposed approach for determining BN structure facilitates the integration of heterogeneous data from multiple sources to elucidate the mechanisms of complex diseases.

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Untitled

Abstract: Ulysses, a new software for the parallel analysis and display of protein interactions detected in various species, is described.

Cited by 21 publications

References 58 publications

Comparative analysis of virus–host interactomes with a mammalian high-throughput protein complementation assay based on Gaussia princeps luciferase

Comparative analysis of virus–host interactomes with a mammalian high-throughput protein complementation assay based on Gaussia princeps luciferase

Protein–Protein Interactions More Conserved within Species than across Species

Identification of Functional Modules by Integration of Multiple Data Sources Using a Bayesian Network Classifier

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