Binding properties and evolution of homodimers in protein-protein interaction networks

Ispolatov, Iaroslav; Yuryev, Anton; Mazo, Ilya; Maslov, Sergei

doi:10.1093/nar/gki678

Cited by 160 publications

(157 citation statements)

References 27 publications

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“…This would suggest a structural difference between the ancient and modern proteome. Because a network's structure can reflect its functional capabilities, such a difference might imply unique functional capabilities of the ancestral proteome or potentially proteomic subfunctionalization between the pre-and postduplication organisms (36)(37)(38). Alternatively, these ohnologous interactions might be de novo.…”

Section: Resultsmentioning

confidence: 99%

The evolutionary dynamics of the Saccharomyces cerevisiae protein interaction network after duplication

Presser

Elowitz

Kellis

et al. 2008

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

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Gene duplication is an important mechanism in the evolution of protein interaction networks. Duplications are followed by the gain and loss of interactions, rewiring the network at some unknown rate. Because rewiring is likely to change the distribution of network motifs within the duplicated interaction set, it should be possible to study network rewiring by tracking the evolution of these motifs. We have developed a mathematical framework that, together with duplication data from comparative genomic and proteomic studies, allows us to infer the connectivity of the preduplication network and the changes in connectivity over time. We focused on the whole-genome duplication (WGD) event in Saccharomyces cerevisiae. The model allowed us to predict the frequency of intergene interaction before WGD and the postduplication probabilities of interaction gain and loss. We find that the predicted frequency of self-interactions in the preduplication network is significantly higher than that observed in today's network. This could suggest a structural difference between the modern and ancestral networks, preferential addition or retention of interactions between ohnologs, or selective pressure to preserve duplicates of self-interacting proteins.gene duplication ͉ network motifs ͉ self-interacting proteins ͉ whole-genome duplication C omplex biological networks result from the evolutionary growth of simpler networks with fewer components. Gene duplication is thought to be a key mechanism by which networks evolve and new components are added (1-6, 43). These duplication events can act on a single gene, a chromosomal segment, or even a whole genome (1, 7-11). After duplication, the duplicate genes may assume one of several fates, including differentiation of sequence and function, or loss of one of the duplicates (12-17, 44). These outcomes are thought to be affected by genetic factors including redundancy, modularization, and expression dosage (9,12,15,(18)(19)(20)(21)(22)45).Little is known about the rules that govern the modification of gene interactions after a duplication event or the effects of gene interaction on the fate of duplicate genes. Here, we report a mathematical framework for inferring the preduplication connectivity properties of a network and for describing its postduplication dynamics. Our method decomposes a protein interaction network into a vector of network motifs and tracks the evolution of this vector over time. We apply our methodology to the protein interaction network of Saccharomyces cerevisiae (23-29), which has undergone a whole-genome duplication (WGD) event, resulting in hundreds of coordinately duplicated gene pairs (ohnologs) (8,9,11).

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Section: Resultsmentioning

confidence: 99%

The evolutionary dynamics of the Saccharomyces cerevisiae protein interaction network after duplication

Presser

Elowitz

Kellis

et al. 2008

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

View full text Add to dashboard Cite

show abstract

“…Large-scale proteomics experiments in yeast, which mainly detect stable cytoplasmic complexes, are enriched for interactions between paralogous proteins (Ispolatov et al 2005;Pereira-Leal et al 2007). This property, often coupled with close genetic linkage, has also been observed for heterophilic IgSF receptor-ligand pairs (Wright et al 2000;Sidorenko and Clark 2003) and is thought to have evolved from the genomic segmental duplication of a gene encoding a protein capable of homophilic association (Williams and Barclay 1988).…”

Section: Extracellular Receptor-ligand Pairs Are Enriched For Paralogsmentioning

confidence: 99%

Large-scale screening for novel low-affinity extracellular protein interactions

Söllner²,

et al. 2008

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Extracellular protein-protein interactions are essential for both intercellular communication and cohesion within multicellular organisms. Approximately a fifth of human genes encode membrane-tethered or secreted proteins, but they are largely absent from recent large-scale protein interaction datasets, making current interaction networks biased and incomplete. This discrepancy is due to the unsuitability of popular high-throughput methods to detect extracellular interactions because of the biochemical intractability of membrane proteins and their interactions. For example, cell surface proteins contain insoluble hydrophobic transmembrane regions, and their extracellular interactions are often highly transient, having half-lives of less than a second. To detect transient extracellular interactions on a large scale, we developed AVEXIS (avidity-based extracellular interaction screen), a high-throughput assay that overcomes these technical issues and can detect very transient interactions (halflives Յ 0.1 sec) with a low false-positive rate. We used it to systematically screen for receptor-ligand pairs within the zebrafish immunoglobulin superfamily and identified novel ligands for both well-known and orphan receptors. Genes encoding receptor-ligand pairs were often clustered phylogenetically and expressed in the same or adjacent tissues, immediately implying their involvement in similar biological processes. Using AVEXIS, we have determined the first systematic low-affinity extracellular protein interaction network, supported by independent biological data. This technique will now allow large-scale extracellular protein interaction mapping in a broad range of experimental contexts.

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“…The ability to self-interact can confer several different structural and functional advantages to proteins, including improved stability, allosteric regulation, control over the accessibility and specificity of active sites, as well as increased complexity (1)(2)(3)(4). In addition, homo-oligomerization can also allow proteins to form large structures without increasing genome size and with increasing error control during synthesis (1,4).…”

mentioning

confidence: 99%

Proteome-wide Prediction of Self-interacting Proteins Based on Multiple Properties

Liu¹,

Guo

Zhang

et al. 2013

Molecular & Cellular Proteomics

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Self-interacting proteins, whose two or more copies can interact with each other, play important roles in cellular functions and the evolution of protein interaction networks (PINs). Knowing whether a protein can self-interact can contribute to and sometimes is crucial for the elucidation of its functions. Previous related research has mainly focused on the structures and functions of specific self-interacting proteins, whereas knowledge on their overall properties is limited. Meanwhile, the two current most common high throughput protein interaction assays have limited ability to detect self-interactions because of biological artifacts and design limitations, whereas the bioinformatic prediction method of self-interacting proteins is lacking. This study aims to systematically study and predict self-interacting proteins from an overall perspective. We find that compared with other proteins the self-interacting proteins in the structural aspect contain more domains; in the evolutionary aspect they tend to be conserved and ancient; in the functional aspect they are significantly enriched with enzyme genes, housekeeping genes, and drug targets, and in the topological aspect tend to occupy important positions in PINs. Furthermore, based on these features, after feature selection, we use logistic regression to integrate six representative features, including Gene Ontology term, domain, paralogous interactor, enzyme, model organism self-interacting protein, and betweenness centrality in the PIN, to develop a proteome-wide prediction model of self-interacting proteins. Using 5-fold cross-validation and an independent test, this model shows good performance. Finally, the prediction model is developed into a user-friendly web service SLIPPER (SeLf-Interacting Protein PrEdictoR). Users may submit a list of proteins, and then SLIPPER will return the probability_scores measuring their possibility to be self-interacting proteins and various related annotation information. This work helps us understand the role self-interacting proteins play in cellular functions from an overall perspective, and the constructed prediction model may contribute to the high throughput finding of selfinteracting proteins and provide clues for elucidating their functions. Molecular & Cellular

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Binding properties and evolution of homodimers in protein-protein interaction networks

Cited by 160 publications

References 27 publications

The evolutionary dynamics of the Saccharomyces cerevisiae protein interaction network after duplication

The evolutionary dynamics of the Saccharomyces cerevisiae protein interaction network after duplication

Large-scale screening for novel low-affinity extracellular protein interactions

Proteome-wide Prediction of Self-interacting Proteins Based on Multiple Properties

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