Qin Sanbo, Zhou Huan-Xiang
Institute of Molecular Biophysics, School of Computational Science, Florida State University, Tallahassee, Florida 32306, USA.
Bioinformatics. 2007 Dec 15;23(24):3386-7. doi: 10.1093/bioinformatics/btm434. Epub 2007 Sep 25.
A number of complementary methods have been developed for predicting protein-protein interaction sites. We sought to increase prediction robustness and accuracy by combining results from different predictors, and report here a meta web server, meta-PPISP, that is built on three individual web servers: cons-PPISP (http://pipe.scs.fsu.edu/ppisp.html), Promate (http://bioportal.weizmann.ac.il/promate), and PINUP (http://sparks.informatics.iupui.edu/PINUP/). A linear regression method, using the raw scores of the three servers as input, was trained on a set of 35 nonhomologous proteins. Cross validation showed that meta-PPISP outperforms all the three individual servers. At coverages identical to those of the individual methods, the accuracy of meta-PPISP is higher by 4.8 to 18.2 percentage points. Similar improvements in accuracy are also seen on CAPRI and other targets.
meta-PPISP can be accessed at http://pipe.scs.fsu.edu/meta-ppisp.html
已经开发了许多用于预测蛋白质-蛋白质相互作用位点的互补方法。我们试图通过组合不同预测器的结果来提高预测的稳健性和准确性，并在此报告一个元网络服务器meta-PPISP，它基于三个独立的网络服务器构建：cons-PPISP（http://pipe.scs.fsu.edu/ppisp.html）、Promate（http://bioportal.weizmann.ac.il/promate）和PINUP（http://sparks.informatics.iupui.edu/PINUP/）。一种使用这三个服务器的原始分数作为输入的线性回归方法，在一组35个非同源蛋白质上进行了训练。交叉验证表明meta-PPISP优于所有三个独立的服务器。在与单个方法相同的覆盖率下，meta-PPISP的准确率高出4.8至18.2个百分点。在CAPRI和其他目标上也观察到了类似的准确率提高。
