Geourjon C, Deléage G
Institut de Biologie et de Chimie des Protéines, UPR 412-CNRS, Lyon, France.
Comput Appl Biosci. 1995 Dec;11(6):681-4. doi: 10.1093/bioinformatics/11.6.681.
Recently a new method called the self-optimized prediction method (SOPM) has been described to improve the success rate in the prediction of the secondary structure of proteins. In this paper we report improvements brought about by predicting all the sequences of a set of aligned proteins belonging to the same family. This improved SOPM method (SOPMA) correctly predicts 69.5% of amino acids for a three-state description of the secondary structure (alpha-helix, beta-sheet and coil) in a whole database containing 126 chains of non-homologous (less than 25% identity) proteins. Joint prediction with SOPMA and a neural networks method (PHD) correctly predicts 82.2% of residues for 74% of co-predicted amino acids. Predictions are available by Email to deleage@ibcp.fr or on a Web page (http:@www.ibcp.fr/predict.html).
最近，一种名为自优化预测方法（SOPM）的新方法被用于提高蛋白质二级结构预测的成功率。在本文中，我们报告了通过预测属于同一家族的一组比对蛋白质的所有序列所带来的改进。这种改进后的SOPM方法（SOPMA）在一个包含126条非同源（同一性小于25%）蛋白质链的完整数据库中，可以正确预测二级结构（α螺旋、β折叠和卷曲）三态描述中69.5%的氨基酸。将SOPMA与神经网络方法（PHD）联合预测，对于74%的共同预测氨基酸，可以正确预测82.2%的残基。预测结果可通过电子邮件发送至deleage@ibcp.fr或在网页（http:@www.ibcp.fr/predict.html）上获取。
