An experimentally derived confidence score for binary protein-protein interactions

By P. Braun, Murat Tasan, Matija Dreze, M. Barrios-Rodiles, I. Lemmens, Haiyuan Yu, Julie M. Sahalie, Ryan R. Murray, Luba Roncari, Anne-Sophie De Smet and 9 more. Nature Methods, 2008

443 citations recorded by the source corpus.

Information on protein-protein interactions is of central importance for many areas of biomedical research. At present no method exists to systematically and experimentally assess the quality of individual interactions reported in interaction mapping experiments. To provide a standardized confidence-scoring method that can be applied to tens of thousands of protein interactions, we have developed an interaction tool kit consisting of four complementary, high-throughput protein interaction assays. We benchmarked these assays against positive and random reference sets consisting of well documented pairs of interacting human proteins and randomly chosen protein pairs, respectively. A logistic regression model was trained using the data from these reference sets to combine the assay outputs and calculate the probability that any newly identified interaction pair is a true biophysical interaction once it has been tested in the tool kit. This general approach will allow a systematic and empirical assignment of confidence scores to all individual protein-protein interactions in interactome networks.

Subfields: Cell Biology, Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/f2a386a3b55d0f80c657c832f6a69b5e44a7632d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cell Biology

In Computational Theory and Mathematics

In Molecular Biology

Citations in the corpus

CORUM: the comprehensive resource of mammalian protein complexes—2009 (2007, background): “Thus, none of the existing methods is able to detect all interactions and it was also shown that even the combined dataset of five different methods missed 40% of experimentally validated, manually curated interactions ( 9 ).”
A reference map of the human binary protein interactome (2020, methodology): “1b, Supplementary Table 3, 4) that showed good sensitivity and low false positive rates when benchmarked against gold-Luck standard positive and random reference sets (PRSv1 and RRSv1, respectively) 15 , while detecting complementary PPI sets (Extended Data Fig.”
Literature-curated protein interaction datasets (2009, methodology): “A GSP should be as unbiased as possible, sampling all, or at least most, parts and processes of the cell 26 , and a GSP must be of the highest reliability and reproducibility 27 .”