Identification of Potential Interaction Networks Using Sequence-Based Searches for Conserved Protein-Protein Interactions or “Interologs”

Matthews, Lisa; Vaglio, Philippe; Reboul, Jérôme; Ge, Hui; Davis, Brian; Garrels, James I.; Vincent, Sylvie; Vidal, Marc

doi:10.1101/gr.205301

Cited by 450 publications

(365 citation statements)

References 24 publications

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“…An indicator of related functions is similar protein-protein interaction patterns. One such special case are the ''interlogs'' (i.e., pairs of interacting proteins that interact identically in two species) (19). It has already been demonstrated that the information about the interacting partners can be used to predict the fold (7,(20)(21)(22) or function (14,(23)(24)(25)(26)(27)) of a protein without considering its sequence.…”

Section: Detecting Remotely Related Proteins By Their Interactions Anmentioning

confidence: 99%

Detecting remotely related proteins by their interactions and sequence similarity

Espadaler

Aragüés²,

Eswar³

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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The function of an uncharacterized protein is usually inferred either from its homology to, or its interactions with, characterized proteins. Here, we use both sequence similarity and protein interactions to identify relationships between remotely related protein sequences. We rely on the fact that homologous sequences share similar interactions, and, therefore, the set of interacting partners of the partners of a given protein is enriched by its homologs. The approach was benchmarked by assigning the fold and functional family to test sequences of known structure. Specifically, we relied on 1,434 proteins with known folds, as defined in the Structural Classification of Proteins (SCOP) database, and with known interacting partners, as defined in the Database of Interacting Proteins (DIP). For this subset, the specificity of fold assignment was increased from 54% for position-specific iterative blast to 75% for our approach, with a concomitant increase in sensitivity for a few percentage points. Similarly, the specificity of family assignment at the e-value threshold of 10-8 was increased from 70% to 87%. The proposed method would be a useful tool for large-scale automated discovery of remote relationships between protein sequences, given its unique reliance on sequence similarity and protein-protein interactions

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Section: Detecting Remotely Related Proteins By Their Interactions Anmentioning

confidence: 99%

Detecting remotely related proteins by their interactions and sequence similarity

Espadaler

Aragüés²,

Eswar³

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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show abstract

“…Another well-known information is homology of PPIs across different species. Methods on this information simply find PPIs which are conserved across species, called interologs (Matthews et al, 2001). Here, any known PPIs regarded as query to find conserved interactions across species using an ortholog database.…”

Section: Computational Prediction Methods For Ppismentioning

confidence: 99%

Protein Interactome and Its Application to Protein Function Prediction

Jung¹,

Jeong²,

Lee³

2012

Protein-Protein Interactions - Computational and Experimental Tools

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“…In Figure 4, we illustrate how even with these caveats, a network constructed in yeast can form a tremendously useful scaffold for understanding the function of Caenorhabditis elegans genes. The networks may thus provide an objective view of gene function that can be transported between organisms, and perhaps iteratively improved with data from across organisms 47,53,54 . Thus, one might consider constructing a universal network, the union of individual organisms' networks, perhaps with linkages and vertices flagged by the organisms from which they derive (such as in the Genome Knowledgebase; http://www.genomeknowledge.org/), that would represent the cumulative body of knowledge of gene function, even across evolutionary divergence of the systems.…”

Section: The Universal Networkmentioning

confidence: 99%

A probabilistic view of gene function

2004

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Cells are controlled by the complex and dynamic actions of thousands of genes. With the sequencing of many genomes, the key problem has shifted from identifying genes to knowing what the genes do; we need a framework for expressing that knowledge. Even the most rigorous attempts to construct ontological frameworks describing gene function (e.g., the Gene Ontology project) ultimately rely on manual curation and are thus labor-intensive and subjective. But an alternative exists: the field of functional genomics is piecing together networks of gene interactions, and although these data are currently incomplete and error-prone, they provide a glimpse of a new, probabilistic view of gene function. We outline such a framework, which revolves around a statistical description of gene interactions derived from large, systematically compiled data sets. In this probabilistic view, pleiotropy is implicit, all data have errors and the definition of gene function is an iterative process that ultimately converges on the correct functions. The relationships between the genes are defined by the data, not by hand. Even this comprehensive view fails to capture key aspects of gene function, not least their dynamics in time and space, showing that there are limitations to the model that must ultimately be addressed.The necessity for a logical framework A primary issue in biology is describing the global organization of genes into systems and understanding the coordination of these systems in the cell. The sequencing of complete genomes has yielded the full parts lists of several organisms, and we now have powerful techniques to sample different aspects of gene function at a genome-wide level. Together, these advances hold great promise in leading us closer to a complete description of the molecular biology of the cell. For us to reach this goal, however, we need some coherent framework in which to express what we learn about gene function through the accumulation of data. What do we mean by 'gene function'? More importantly, what should we mean by this? What are the key properties of genes that we should measure to adequately describe their function in the cell? If we have all the measurements, how should we integrate them into a complete description of the molecular machineries that are the basis of a cell? Our framework for thinking about gene function inevitably colors the questions we ask and the conclusions we draw; therefore, defining a rational framework is not merely an exercise in abstraction but a key tool in understanding how an organism works.Great effort (e.g., refs. 1-8) has already gone into defining precisely what we mean by the functions of genes, as well as how we should record this information. These summaries help us by identifying common features of genes with similar functions and giving clues to the organization and control of processes in the cell. They may also help us to see whether this organization can explain observed biological properties and guide us toward new processes. The functional framework itsel...

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Identification of Potential Interaction Networks Using Sequence-Based Searches for Conserved Protein-Protein Interactions or “Interologs”

Cited by 450 publications

References 24 publications

Detecting remotely related proteins by their interactions and sequence similarity

Detecting remotely related proteins by their interactions and sequence similarity

Protein Interactome and Its Application to Protein Function Prediction

A probabilistic view of gene function

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