Authors
Nevan J Krogan, Gerard Cagney, Haiyuan Yu, Gouqing Zhong, Xinghua Guo, Alexandr Ignatchenko, Joyce Li, Shuye Pu, Nira Datta, Aaron P Tikuisis, Thanuja Punna, José M Peregrín-Alvarez, Michael Shales, Xin Zhang, Michael Davey, Mark D Robinson, Alberto Paccanaro, James E Bray, Anthony Sheung, Bryan Beattie, Dawn P Richards, Veronica Canadien, Atanas Lalev, Frank Mena, Peter Wong, Andrei Starostine, Myra M Canete, James Vlasblom, Samuel Wu, Chris Orsi, Sean R Collins, Shamanta Chandran, Robin Haw, Jennifer J Rilstone, Kiran Gandi, Natalie J Thompson, Gabe Musso, Peter St Onge, Shaun Ghanny, Mandy HY Lam, Gareth Butland, Amin M Altaf-Ul, Shigehiko Kanaya, Ali Shilatifard, Erin O'Shea, Jonathan S Weissman, C James Ingles, Timothy R Hughes, John Parkinson, Mark Gerstein, Shoshana J Wodak, Andrew Emili, Jack F Greenblatt
Publication date
2006/3/30
Journal
Nature
Volume
440
Issue
7084
Pages
637-643
Publisher
Nature Publishing Group UK
Description
Identification of protein–protein interactions often provides insight into protein function, and many cellular processes are performed by stable protein complexes. We used tandem affinity purification to process 4,562 different tagged proteins of the yeast Saccharomyces cerevisiae. Each preparation was analysed by both matrix-assisted laser desorption/ionization–time of flight mass spectrometry and liquid chromatography tandem mass spectrometry to increase coverage and accuracy. Machine learning was used to integrate the mass spectrometry scores and assign probabilities to the protein–protein interactions. Among 4,087 different proteins identified with high confidence by mass spectrometry from 2,357 successful purifications, our core data set (median precision of 0.69) comprises 7,123 protein–protein interactions involving 2,708 proteins. A Markov clustering algorithm organized these interactions into 547 …
Total citations
200620072008200920102011201220132014201520162017201820192020202120222023202469264296270307310290241225180170145121130106117985344