iWRAP: An interface threading approach with application to prediction of cancer-related protein-protein interactions.
iWRAP: An interface threading approach with application to prediction of cancer-related protein-protein interactions.
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DOI：
10.1016/j.jmb.2010.11.025
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发表时间：
2011-02-04
影响因子：
5.6
通讯作者：
Berger B
中科院分区：
文献类型：
--
作者：
Hosur R;Xu J;Bienkowska J;Berger B
目前用于预测蛋白质-蛋白质相互作用(PPI)的同源建模方法很难在序列同一性的“暮光区”(<；40%)。穿线方法通过将一对蛋白质的初级序列与最适合的模板复合体进行比对来预测整个三维结构，从而将覆盖范围进一步扩展到黄昏区域。我们介绍了一种线程化方法iWRAP，它只关注蛋白质界面。我们的方法结合了用于界面对齐的新的线性规划公式和用于交互预测的增强分类器。我们在蛋白质数据库中对PPI进行分类的SCOPPI和整个酵母基因组上展示了它的有效性。IWRAP在严格的SCOPPI交叉验证中提供了对PPI及其接口的显著改进的预测。此外，通过将我们的预测与全复合穿线器相结合，我们实现了酵母PPI的覆盖率为13%，这比以前的方法在更高的灵敏度下增加了近50%。作为应用，我们有效地将iWRAP与基因组数据相结合，以识别与染色质重塑、核小体组织和核核复合体组装有关的新的癌症相关基因。IWRAP可在http://iwrap.csail.mit.edu.上获得
Current homology modeling methods for predicting protein-protein interactions (PPIs) have difficulty in the “twilight zone” (<40%) of sequence identities. Threading methods extend coverage further into the twilight zone by aligning primary sequences for a pair of proteins to a best-fit template complex to predict an entire three-dimensional structure. We introduce a threading approach, iWRAP, which focuses on only the protein interface. Our approach combines a novel linear programming formulation for interface alignment with a boosting classifier for interaction prediction. We demonstrate its efficacy on SCOPPI, a classification of PPIs in the Protein Databank, and on the entire yeast genome. iWRAP provides significantly improved prediction of PPIs and their interfaces in stringent cross-validation on SCOPPI. Furthermore, by combining our predictions with a full-complex threader, we achieve coverage of 13% for the yeast PPIs, which is close to a 50% increase over previous methods at a higher sensitivity. As an application, we effectively combine iWRAP with genomic data to identify novel cancer related genes involved in chromatin remodeling, nucleosome organization and ribonuclear complex assembly. iWRAP is available at http://iwrap.csail.mit.edu.
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影响因子：
7
作者：
Deng, MH;Mehta, S;Chen, T
通讯作者：
Chen, T
影响因子：
4.3
作者：
Bandyopadhyay S;Kelley R;Krogan NJ;Ideker T
通讯作者：
Ideker T
DOI：
10.1093/bioinformatics/btn615
发表时间：
2009-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Carbon S;Ireland A;Mungall CJ;Shu S;Marshall B;Lewis S;AmiGO Hub;Web Presence Working Group
通讯作者：
Web Presence Working Group
DOI：
10.1073/pnas.092147999
发表时间：
2002-04-30
影响因子：
11.1
作者：
Aloy, P;Russell, RB
通讯作者：
Russell, RB
影响因子：
5.8
作者：
Capra, John A.;Singh, Mona
通讯作者：
Singh, Mona