Interaction Viewer

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Interaction Details

Accession
EBI-11020127
Detection Method
anti tag coip
Positive Interaction
✔️
External Cross References (2)
DatabaseIdentifier
pride PXD002815
imex
Annotations (9)
TopicDescription
url http://www.biochem.mpg.de/mann/interactome/MCP_ky_0002819
exp-modification Author confidence calculation Protein identifications as obtained from MS spectra processing were filtered, removing hits to the reverse decoy database as well as proteins only identified by modified peptides. It was required that each protein be quantified in all replicates from the AP-MS samples of at least one cell line. Protein LFQ intensities were logarithmized and missing values imputed by values simulating noise around the detection limit. For each protein, a non-parametric method was used to select a subset of samples that provide a distribution of background intensities for this protein. This subset was used first to normalize all protein intensities to represent relative enrichment, and then to serve as the control group for a two-tailed Welch’s t test. Specific outliers in the volcano plots of logarithmized p values against enrichments were determined by an approach making use of the asymmetry in the outlier population (see url links for each interaction). Two cut-offs of different stringencies, representing 1 and 5% of enrichment false discovery rate (FDR), respectively, were used. Correlation coefficients between the intensity profiles of interacting proteins were calculated as additional quality parameters. Enrichment FDR (classes A, B and C) and profile correlation (modifier + or –) define the confidence class of an interaction (see author assigned scores). Relative abundance and interaction stoichiometry estimations: Estimating interaction stoichiometries requires the comparison of the amounts of different proteins relative to each other in one IP. To this end, the authors first subtracted the median intensity across all samples to account for the proportion due to unspecific binding. Then LFQ intensities were divided by the number of theoretically observable peptides for this protein. This corrects for biases introduced by different lengths of the protein sequences and the frequency and distribution of proteolytic cleavage sites. Finally, interaction stoichiometries were expressed relative to the bait protein. Cellular copy numbers and relative abundances were calculated using a similar approach on the whole proteome data and brought to absolute scale by normalization to a total protein amount of 200 pg in a cell volume of 1 pl for a HeLa cell.
accepted Accepted 2015-OCT-15 AT 13:40 BST AT 13:40 BST by ORCHARD
correction comment Corrected. SILAC data situation explained in data processing comment.; All done and silac data explained as data-processing comment.
comment Cell line id: MCP_ky_0002819
curation depth imex curation
figure legend Suppl. table S2. For bait details see supp. table S1
author-announcement 21-10-2015: Contacted by IntAct-Help.
data-processing PAM-SILAC experiments mentioned in the text and summarized in figure 5 were not added since the authors stated that the SILAC data just confirmed the large screening results for a set of selected baits and did not provide further detail on the interactions. The cell lines tested with PAM-SILAC were MCP_ky_0002211 (interaction id EBI-11003881), MCP_ky_0006158 (interaction id EBI-11131339) and MCP_ky_0003099 (interaction id EBI-11029579).

Publication

Title
A human interactome in three quantitative dimensions organized by stoichiometries and abundances.
Journal
Cell
Authors
Hein MY.,Hubner NC.,Poser I.,Cox J.,Nagaraj N.,Toyoda Y.,Gak IA.,Weisswange I.,Mansfeld J.,Buchholz F.,Hyman AA.,Mann M.
Publication date
01/01/2015
Publication reference
PubMed: 26496610
External Cross References (1)
DatabaseIdentifier
imex
Annotations (2)
TopicDescription
contact-email hein@biochem.mpg.de,mmann@biochem.mpg.de
author submitted 09-06-2015: Submitted by Marco Hein, Max Planck Institute of Biochemistry, Martinsried.