Efficient prediction of human protein-protein interactions at a global scale

Schoenrock, Andrew; Samanfar, Bahram; Pitre, Sylvain; Hooshyar, M. A.; Jin, Ke; Phillips, Charles; Wang, Hui; Phanse, Sadhna; Omidi, Katayoun; Gui, Yuan; Alamgir,; Wong, Alex; Barrenäs, Fredrik; Babu, Mohan; Benson, Mikael; Langston, Michael A.; Green, James R.; Dehne, Frank; Golshani, Ashkan

doi:10.1186/s12859-014-0383-1

Cited by 41 publications

(43 citation statements)

References 90 publications

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“…We have used the PIPE algorithm [32–36] to predict interactomes for all five species, and we provide a null model for PPI network evolution by simulation. We find evidence for extensive conservation of PPIs, as might be expected given the importance of PPIs for basic cellular functions.…”

Section: Discussionmentioning

confidence: 99%

“…These drawbacks include a reliance on unavailable or unreliable biological data (evolutionary history, domains, 3D structure, etc. ), high computational complexity leading to excessive run times, and unacceptably high error rates, among others [36]. …”

Section: Methodsmentioning

confidence: 99%

“…To remain consistent across all predictions made, this decision threshold was used across all strains, both real and simulated. For more details on how these LOOCV tests are conducted see [36]. …”

Section: Methodsmentioning

confidence: 99%

“…Our computational inference makes use of the Protein-protein Interaction Prediction Engine (PIPE), an algorithm that predicts PPIs on the basis of protein primary sequence only [32–36]. PIPE breaks query proteins into short overlapping polypeptide segments and searches within a list of known and experimentally verified PPIs to find similar segments.…”

Section: Introductionmentioning

confidence: 99%

“…Our ability to achieve such high specificity is a distinguishing feature of PIPE [37], and is critical when one intends to examine millions of protein pairs (effectively testing millions of hypotheses). PIPE has been used to identify novel protein interactions, to discover new protein complexes, to predict novel protein functions [32–36], and to produce proteome-wide predicted interaction networks for S . cerevisiae [34], Schizosaccharomyces pombe [33], Caenorhabditis elegans [35] and Homo sapiens [36], among others.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evolution of protein-protein interaction networks in yeast

et al. 2017

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Current Experimental Methods for Characterizing Protein–Protein Interactions

2016

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Protein molecules often interact with other partner protein molecules in order to execute their vital functions in living organisms. Characterization of protein-protein interactions thus plays a central role in understanding the molecular mechanism of relevant protein molecules, elucidating the cellular processes and pathways relevant to health or disease for drug discovery, and charting large-scale interaction networks in systems biology research. A whole spectrum of methods, based on biophysical, biochemical, or genetic principles, have been developed to detect the time, space, and functional relevance of protein-protein interactions at various degrees of affinity and specificity. This article presents an overview of these experimental methods, outlining the principles, strengths and limitations, and recent developments of each type of method.

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Mapping and identification of a potential candidate gene for a novel maturity locus, E10, in soybean

et al. 2016

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E10 is a new maturity locus in soybean and FT4 is the predicted/potential functional gene underlying the locus. Flowering and maturity time traits play crucial roles in economic soybean production. Early maturity is critical for north and west expansion of soybean in Canada. To date, 11 genes/loci have been identified which control time to flowering and maturity; however, the molecular bases of almost half of them are not yet clear. We have identified a new maturity locus called "E10" located at the end of chromosome Gm08. The gene symbol E10e10 has been approved by the Soybean Genetics Committee. The e10e10 genotype results in 5-10 days earlier maturity than E10E10. A set of presumed E10E10 and e10e10 genotypes was used to identify contrasting SSR and SNP haplotypes. These haplotypes, and their association with maturity, were maintained through five backcross generations. A functional genomics approach using a predicted protein-protein interaction (PPI) approach (Protein-protein Interaction Prediction Engine, PIPE) was used to investigate approximately 75 genes located in the genomic region that SSR and SNP analyses identified as the location of the E10 locus. The PPI analysis identified FT4 as the most likely candidate gene underlying the E10 locus. Sequence analysis of the two FT4 alleles identified three SNPs, in the 5'UTR, 3'UTR and fourth exon in the coding region, which result in differential mRNA structures. Allele-specific markers were developed for this locus and are available for soybean breeders to efficiently develop earlier maturing cultivars using molecular marker assisted breeding.

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Efficient prediction of human protein-protein interactions at a global scale

Cited by 41 publications

References 90 publications

Evolution of protein-protein interaction networks in yeast

Evolution of protein-protein interaction networks in yeast

Current Experimental Methods for Characterizing Protein–Protein Interactions

Mapping and identification of a potential candidate gene for a novel maturity locus, E10, in soybean

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