PIPE: a protein-protein interaction prediction engine based on the re-occurring short polypeptide sequences between known interacting protein pairs. Background: Identification 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. Results: Here 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. Conclusion: PIPE 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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  1. Zhang, Ya-Nan; Pan, Xiao-Yong; Huang, Yan; Shen, Hong-Bin: Adaptive compressive learning for prediction of protein-protein interactions from primary sequence (2011)
  2. Chang, Darby Tien-Hao; Syu, Yu-Tang; Lin, Po-Chang: Predicting the protein-protein interactions using primary structures with predicted protein surface (2010) ioport
  3. Sobolev, Boris; Filimonov, Dmitry; Lagunin, Alexey; Zakharov, Alexey; Koborova, Olga; Kel, Alexander E.; Poroikov, Vladimir: Functional classification of proteins based on projection of amino acid sequences: application for prediction of protein kinase substrates (2010) ioport
  4. Andreopoulos, Bill; Winter, Christof; Labudde, Dirk; Schroeder, Michael: Triangle network motifs predict complexes by complementing high-error interactomes with structural information (2009) ioport
  5. Park, Yungki: Critical assessment of sequence-based protein-protein interaction prediction methods that do not require homologous protein sequences (2009) ioport
  6. Zhang, Kelvin Xi; Ouellette, B. F. Francis: GAIA: a gram-based interaction analysis tool - an approach for identifying interacting domains in yeast (2009) ioport