生命系統(tǒng)復(fù)雜性最重要的特征不僅在于其組成成分的復(fù)雜性,，更在于各組成成分之間的關(guān)系,，而在所有的這些關(guān)系中，蛋白質(zhì)之間的相互作用在形成幾乎所有生命系統(tǒng),、調(diào)控各種生理/病理進(jìn)程中發(fā)揮至關(guān)重要的作用,。近來(lái),，人們發(fā)展了許多高通量的實(shí)驗(yàn)方法，并以此發(fā)現(xiàn),、建立了越來(lái)越多的蛋白質(zhì)相互作用網(wǎng)絡(luò),。但是，現(xiàn)有的蛋白質(zhì)相互作用網(wǎng)絡(luò)只能反映蛋白質(zhì)之間存在連接關(guān)系,，而真實(shí)生命體系中大部分的蛋白質(zhì)相互作用具有信號(hào)轉(zhuǎn)導(dǎo),、轉(zhuǎn)錄激活/抑制等明顯的信號(hào)流方向性。準(zhǔn)確,、高效,、大規(guī)模預(yù)測(cè)蛋白質(zhì)相互作用網(wǎng)絡(luò)中相互作用的信號(hào)流方向、發(fā)現(xiàn)潛在的信號(hào)轉(zhuǎn)導(dǎo)通路,，為眾多領(lǐng)域的生物科學(xué)工作者所期待,，但一直是未能破解的世界性難題。
北京蛋白質(zhì)組研究中心朱云平研究員,、賀福初院士課題組劉偉博士等第一次提出蛋白質(zhì)間結(jié)構(gòu)域的相互作用在一定程度上決定了它們之間的信號(hào)流走向的假設(shè),；進(jìn)而基于這個(gè)假設(shè)發(fā)展了一種新的理論方法來(lái)預(yù)測(cè)蛋白質(zhì)組網(wǎng)絡(luò)中信號(hào)流的方向性，并被成功地用于網(wǎng)絡(luò)中未知信號(hào)轉(zhuǎn)導(dǎo)通路的規(guī)?；诰?。該方法不需要其它任何先驗(yàn)知識(shí)，僅根據(jù)蛋白質(zhì)中包含的結(jié)構(gòu)域就可以快速地推斷出相互作用蛋白質(zhì)間的信號(hào)流方向,、系統(tǒng)地發(fā)掘出蛋白質(zhì)組網(wǎng)絡(luò)中潛在的大量信號(hào)通路,。該方法應(yīng)用范圍廣泛，其預(yù)測(cè)結(jié)果可為實(shí)驗(yàn)研究提供重要的研究線索,。該方法已被成功地用于大規(guī)模預(yù)測(cè)和揭示人類蛋白質(zhì)組成員間相互作用的信號(hào)流方向,，并構(gòu)建第一個(gè)具有明確作用方向的人蛋白質(zhì)組相互作用網(wǎng)絡(luò)，揭示了大量未知的信號(hào)轉(zhuǎn)導(dǎo)通路,。它們將為人們理解生命系統(tǒng)中的信息網(wǎng)絡(luò)等重要理論問(wèn)題提供全新的視野,。相關(guān)工作近期在線發(fā)表于《分子與細(xì)胞蛋白質(zhì)組學(xué)》。（生物谷Bioon.com）
生物谷推薦原始出處：
Mol. Cell. Proteomics, Jun 2009; doi:10.1074/mcp.M800354-MCP200
Proteome-wide prediction of signal flow direction in protein interaction networks based on interacting domains
Wei Liu, Dong Li, Jian Wang, Hongwei Xie, Yunping Zhu, and Fuchu He
1 the State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Radiation Medicine, Beijing, Changping 102206
Signal flow direction is one of the most important features of the protein-protein interactions (PPIs) in signalling networks. However, almost all the outcomes of current high-throughout techniques for PPI mapping are usually supposed to be non-directional. Based on the pairwise interaction domains, here we defined a novel parameter Protein Interaction Directional Score (PIDS) and then used it to predict the direction of signal flow between proteins in proteome-wide signaling networks. Using 5-fold cross-validation, our approach obtained a satisfied performance with the accuracy 89.79%, coverage 48.08% and error ratio 16.91%. As an application, we established an integrated human Directional Protein Interaction Network (DPIN), including 2,237 proteins and 5,530 interactions, and inferred a large amount of novel signaling pathways. DPIN was strongly supported by the known signaling pathways literature (with the 87.5% accuracy), and further analyses on the biological annotation, subcellular localization and network topology property. Thus, this study provided an effective method to define the upstream/downstream relations of interacting protein pairs, and a powerful tool to unravel the unknown signaling pathways.
