ArticleBMC bioinformatics2006
PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers.
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68 citing papers in PubMed, 217 citations in OpenAlex.
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- High-Throughput Interactome Determination via Sulfur Anomalous Scattering.The journal of physical chemistry letters · 2024Article
- DL-PPI: a method on prediction of sequenced protein-protein interaction based on deep learning.BMC bioinformatics · 2023Article
- In silico protein function prediction: the rise of machine learning-based approaches.Medical review (2021) · 2023Review
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- Large-scale data mining pipeline for identifying novel soybean genes involved in resistance against the soybean cyst nematode.Frontiers in bioinformatics · 2023Article
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
- A computational approach to rapidly design peptides that detect SARS-CoV-2 surface protein S.NAR genomics and bioinformatics · 2022Article
- PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.Journal of molecular biology · 2022Article
- Cryo-EM structures of Gid12-bound GID E3 reveal steric blockade as a mechanism inhibiting substrate ubiquitylation.Nature communications · 2022Article
- Adaptor Protein ShcD/Molecular cancer research : MCR · 2021Article
- Multi-schema computational prediction of the comprehensive SARS-CoV-2 vs. human interactome.PeerJ · 2021Article
- Identification of self-interacting proteins by integrating random projection classifier and finite impulse response filter.BMC genomics · 2019Article
- ProtDCal-Suite: A web server for the numerical codification and functional analysis of proteins.Protein science : a publication of the Protein Society · 2019Article
- Computational identification of protein-protein interactions in model plant proteomes.Scientific reports · 2019Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIdentification of protein interaction networks has received considerable attention in the post-genomic era. The currently available biochemical approaches used to detect protein-protein interactions are all time and labour intensive. Consequently there is a growing need for the development of computational tools that are capable of effectively identifying such interactions.
resultsHere we explain the development and implementation of a novel Protein-Protein Interaction Prediction Engine termed PIPE. This tool is capable of predicting protein-protein interactions for any target pair of the yeast Saccharomyces cerevisiae proteins from their primary structure and without the need for any additional information or predictions about the proteins. PIPE showed a sensitivity of 61% for detecting any yeast protein interaction with 89% specificity and an overall accuracy of 75%. This rate of success is comparable to those associated with the most commonly used biochemical techniques. Using PIPE, we identified a novel interaction between YGL227W (vid30) and YMR135C (gid8) yeast proteins. This lead us to the identification of a novel yeast complex that here we term vid30 complex (vid30c). The observed interaction was confirmed by tandem affinity purification (TAP tag), verifying the ability of PIPE to predict novel protein-protein interactions. We then used PIPE analysis to investigate the internal architecture of vid30c. It appeared from PIPE analysis that vid30c may consist of a core and a secondary component. Generation of yeast gene deletion strains combined with TAP tagging analysis indicated that the deletion of a member of the core component interfered with the formation of vid30c, however, deletion of a member of the secondary component had little effect (if any) on the formation of vid30c. Also, PIPE can be used to analyse yeast proteins for which TAP tagging fails, thereby allowing us to predict protein interactions that are not included in genome-wide yeast TAP tagging projects.
conclusionPIPE analysis can predict yeast protein-protein interactions. Also, PIPE analysis can be used to study the internal architecture of yeast protein complexes. The data also suggests that a finite set of short polypeptide signals seem to be responsible for the majority of the yeast protein-protein interactions.
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