Chou Kuo-Chen, Shen Hong-Bin
Gordon Life Science Institute, 13784 Torrey Del Mar Drive, San Diego, CA 92130, USA.
Biochem Biophys Res Commun. 2007 Jun 8;357(3):633-40. doi: 10.1016/j.bbrc.2007.03.162. Epub 2007 Apr 5.
We have developed an automated method for predicting signal peptide sequences and their cleavage sites in eukaryotic and bacterial protein sequences. It is a 2-layer predictor: the 1st-layer prediction engine is to identify a query protein as secretory or non-secretory; if it is secretory, the process will be automatically continued with the 2nd-layer prediction engine to further identify the cleavage site of its signal peptide. The new predictor is called Signal-CF, where C stands for "coupling" and F for "fusion", meaning that Signal-CF is formed by incorporating the subsite coupling effects along a protein sequence and by fusing the results derived from many width-different scaled windows through a voting system. Signal-CF is featured by high success prediction rates with short computational time, and hence is particularly useful for the analysis of large-scale datasets. Signal-CF is freely available as a web-server at http://chou.med.harvard.edu/bioinf/Signal-CF/ or http://202.120.37.186/bioinf/Signal-CF/.
我们已经开发出一种自动方法，用于预测真核生物和细菌蛋白质序列中的信号肽序列及其切割位点。它是一个两层预测器：第一层预测引擎用于识别查询蛋白质是分泌型还是非分泌型；如果是分泌型，该过程将自动进入第二层预测引擎，以进一步识别其信号肽的切割位点。这种新的预测器称为Signal-CF，其中C代表“耦合”，F代表“融合”，这意味着Signal-CF是通过整合沿蛋白质序列的亚位点耦合效应，并通过投票系统融合来自许多不同宽度缩放窗口的结果而形成的。Signal-CF的特点是预测成功率高且计算时间短，因此对于大规模数据集的分析特别有用。Signal-CF可作为网络服务器免费获取，网址为http://chou.med.harvard.edu/bioinf/Signal-CF/ 或http://202.120.37.186/bioinf/Signal-CF/ 。
