Accurate and automated classification of protein secondary structure with PsiCSI
Accurate and automated classification of protein secondary structure with PsiCSI
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DOI：
10.1110/ps.0222303
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发表时间：
2003-02-01
期刊：
影响因子：
8
通讯作者：
Samudrala, R
中科院分区：
文献类型：
--
作者：
Hung, LH;Samudrala, R
PsiCSI是一种高度准确和自动化的方法，用于从NMR数据中指定二级结构，这是确定三级结构的有用中间步骤。该方法使用三层神经网络将来自化学位移和蛋白质序列的信息相结合。训练和测试是在一套92种蛋白质（9437个残基）上进行的，这些蛋白质具有已知的二级和三级结构。使用严格的交叉验证程序，其中从用于训练神经网络的数据库中去除靶蛋白和同源蛋白，观察到平均89%Q3准确度（每个残基）。这比单独使用化学位移（CSI）或序列信息（Psipred）的方法增加了6.2%和5.5%（代表36%和33%的错误）。此外，PsiCSI改进了化学位移信息到二级结构的转化（Q3 = 87.4%），并且能够使用序列信息作为稀疏NMR数据的有效替代（Q3 = 86.9%，没有C-13位移，Q3 = 86.8%，只有H）。可用的班次）。最后，PsiCSI所犯的错误几乎只涉及螺旋或链与螺旋的互换，而不是螺旋与链的互换（
PsiCSI is a highly accurate and automated method of assigning secondary structure from NMR data, which is a useful intermediate step in the determination of tertiary structures. The method combines information from chemical shifts and protein sequence using three layers of neural networks. Training and testing was performed on a suite of 92 proteins (9437 residues) with known secondary and tertiary structure. Using a stringent cross-validation procedure in which the target and homologous proteins were removed from the databases used for training the neural networks, an average 89% Q3 accuracy (per residue) was observed. This is an increase of 6.2% and 5.5% (representing 36% and 33% fewer errors) over methods that use chemical shifts (CSI) or sequence information (Psipred) alone. In addition, PsiCSI improves upon the translation of chemical shift information to secondary structure (Q3 = 87.4%) and is able to use sequence information as an effective substitute for sparse NMR data (Q3 = 86.9% without C-13 shifts and Q3 = 86.8% with only H. shifts available). Finally, errors made by PsiCSI almost exclusively involve, the interchange of helix or strand with coil and not helix with strand (