生物谷報道：釀酒酵母蛋白質(zhì)復(fù)合體全景分析
生物有機體大量的細胞過程由穩(wěn)定的蛋白質(zhì)復(fù)合體執(zhí)行，蛋白質(zhì)-蛋白質(zhì)相互作用的鑒定可以更深入的了解蛋白質(zhì)的功能,。實驗采用串聯(lián)親和純化法純化4562個不同的釀酒酵母標(biāo)記蛋白,。每次純化均采用基質(zhì)輔助激光解析電離質(zhì)譜技術(shù)和離子交換串聯(lián)質(zhì)譜技術(shù)（LCTMS）增加處理的準(zhǔn)確度和覆蓋面。智能機器分析法（Machine learning）對質(zhì)譜總譜和assign probabilities求積分從而獲得蛋白質(zhì)—蛋白質(zhì)相互關(guān)系系數(shù),，高通量質(zhì)譜技術(shù)對4087個蛋白質(zhì)純化有2357成功,。核心數(shù)據(jù)庫(0.69%精度）構(gòu)成為2708個復(fù)合體7123處蛋白相互作用。利用馬爾可夫分析法(Markov)對這些相關(guān)點重組成的547個蛋白復(fù)合體(4.9個二級單位/復(fù)合體)進行分析,，約有半數(shù)在MIPS數(shù)據(jù)庫里不顯示,，429 additional interactions也顯示空缺。數(shù)據(jù)對單個蛋白分子,、功能基因組學(xué)和系統(tǒng)生物學(xué)的研究是十分有用的,。
原始出處：
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 H. Y. 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 and Jack F. Greenblatt. Global landscape of protein complexes in the yeast Saccharomyces cerevisiae .
原始摘要: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 protein complexes averaging 4.9 subunits per complex, about half of them absent from the MIPS database, as well as 429 additional interactions between pairs of complexes. The data (all of which are available online) will help future studies on individual proteins as well as functional genomics and systems biology.
連接:Global landscape of protein complexes in the yeast Saccharomyces cerevisiae
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蛋白質(zhì)組將為腫瘤早診“撕開”突破口
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Nature：華人科學(xué)家施一公揭示蛋白質(zhì)相互作用調(diào)控細胞凋亡的結(jié)構(gòu)學(xué)基礎(chǔ)
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日本科學(xué)家發(fā)現(xiàn)可檢測腦腫瘤惡性度蛋白質(zhì)
