A Conserved Mammalian Protein Interaction Network

Pérez-Bercoff, Åsa; Hudson, Corey; Conant, Gavin C.

doi:10.1371/journal.pone.0052581

Cited by 11 publications

(8 citation statements)

References 58 publications

(80 reference statements)

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“…We identified 936 unique proteins that are inferred to experience fewer changes than expected in their interaction profiles across the phylogeny. This is not surprising as multiple PPI networks have been reported as being extremely conserved across both closely and distantly related phylogenies [19, 49–51]. However, given the species examined, our data provide unique insight into networks conserved over shorter evolutionary distances.…”

Section: Discussionsupporting

confidence: 59%

Evolution of protein-protein interaction networks in yeast

et al. 2017

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Interest in the evolution of protein-protein and genetic interaction networks has been rising in recent years, but the lack of large-scale high quality comparative datasets has acted as a barrier. Here, we carried out a comparative analysis of computationally predicted protein-protein interaction (PPI) networks from five closely related yeast species. We used the Protein-protein Interaction Prediction Engine (PIPE), which uses a database of known interactions to make sequence-based PPI predictions, to generate high quality predicted interactomes. Simulated proteomes and corresponding PPI networks were used to provide null expectations for the extent and nature of PPI network evolution. We found strong evidence for conservation of PPIs, with lower than expected levels of change in PPIs for about a quarter of the proteome. Furthermore, we found that changes in predicted PPI networks are poorly predicted by sequence divergence. Our analyses identified a number of functional classes experiencing fewer PPI changes than expected, suggestive of purifying selection on PPIs. Our results demonstrate the added benefit of considering predicted PPI networks when studying the evolution of closely related organisms.

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Section: Discussionsupporting

confidence: 59%

Evolution of protein-protein interaction networks in yeast

et al. 2017

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“…Indeed, genes acting at the centre of protein–protein interaction networks (PINs) and metabolic networks (i.e., genes coding for proteins with many interactions or connections) evolve under higher levels of purifying selection than those acting at the network periphery ( Fraser et al 2002 ; Hahn and Kern 2005 ; Vitkup et al 2006 ; Alvarez-Ponce 2012 ; Alvarez-Ponce and Fares 2012 ) (but see Jordan et al 2003 ; Hahn et al 2004 ). Furthermore, interacting proteins evolve at similar rates, probably as a result of molecular coevolution ( Fraser et al 2002 ; Agrafioti et al 2005 ; Codoñer and Fares 2008 ; Cui et al 2009 ; Lovell and Robertson 2010 ; Pérez-Bercoff et al 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Recent Positive Selection Has Acted on Genes Encoding Proteins with More Interactions within the Whole Human Interactome

Luisi

Alvarez‐Ponce

Pybus

et al. 2015

Genome Biology and Evolution

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Genes vary in their likelihood to undergo adaptive evolution. The genomic factors that determine adaptability, however, remain poorly understood. Genes function in the context of molecular networks, with some occupying more important positions than others and thus being likely to be under stronger selective pressures. However, how positive selection distributes across the different parts of molecular networks is still not fully understood. Here, we inferred positive selection using comparative genomics and population genetics approaches through the comparison of 10 mammalian and 270 human genomes, respectively. In agreement with previous results, we found that genes with lower network centralities are more likely to evolve under positive selection (as inferred from divergence data). Surprisingly, polymorphism data yield results in the opposite direction than divergence data: Genes with higher centralities are more likely to have been targeted by recent positive selection during recent human evolution. Our results indicate that the relationship between centrality and the impact of adaptive evolution highly depends on the mode of positive selection and/or the evolutionary time-scale.

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“…Perez‐Bercoff et al. [] compared PPIs in humans and seven other mammals. They concluded that in mouse and rat, 88% and 75%, respectively, of the human PPIs are conserved.…”

Section: Network Biologymentioning

confidence: 99%

Biological Networks and Drug Discovery—Where Do We Stand?

Geppert

Koeppen

2014

Drug Development Research

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The progress in genomics and molecular biology during the last decade has led to the insight that complex diseases are caused by defects in many genes and that cellular processes are organized in complex networks. In the search of new drugs, these findings have not yet been fully taken into account. The currently predominating drug discovery approach in pharmaceutical industry is still target-centric, with the goal to optimize compounds toward high affinity and selectivity for a specific target. There is growing evidence that the successful development of effective disease-modifying medications needs a network approach instead. This might also help to reduce the very high failure rate in the current drug development process. This review outlines concepts and applications of network biology and routes toward drugs that are designed to interact with several targets. Obstacles to the implementation of this approach in the pharmaceutical industry are discussed.

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A Conserved Mammalian Protein Interaction Network

Cited by 11 publications

References 58 publications

Evolution of protein-protein interaction networks in yeast

Evolution of protein-protein interaction networks in yeast

Recent Positive Selection Has Acted on Genes Encoding Proteins with More Interactions within the Whole Human Interactome

Biological Networks and Drug Discovery—Where Do We Stand?

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