PREDICTION OF PROTEIN SECONDARY STRUCTURE AT BETTER THAN 70-PERCENT ACCURACY
PREDICTION OF PROTEIN SECONDARY STRUCTURE AT BETTER THAN 70-PERCENT ACCURACY
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DOI：
10.1006/jmbi.1993.1413
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发表时间：
1993-07-20
影响因子：
5.6
通讯作者：
SANDER, C
中科院分区：
文献类型：
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作者：
ROST, B;SANDER, C
我们在130个蛋白质链的非冗余数据库上训练了一个双层前馈神经网络来预测水溶性蛋白质的二级结构。一个新的关键方面是以多序列比对的形式使用进化信息，作为代替单一序列的输入。以这种形式包含蛋白质家族信息可使预测精度提高6 - 8个百分点。三个级别网络的组合导致球形蛋白质的总体三状态精度为70.8%（持续性能）。如果将4条膜蛋白链纳入评估，则总体准确率降至70.2%。α-螺旋、β-链和环之间的预测很好地平衡：65%的观察到的链残基预测正确。预测三种二级结构类型含量的准确度与圆二色光谱相当。通过七倍交叉验证测试和对26个最近解决的蛋白质的额外测试，验证了性能的准确性。具体位置可靠性指标的定义具有特殊的实际重要性。对于高可靠性预测的残差的一半，总体精度提高到82%以上。该方法的另一个优点是对片段长度的预测更加真实。该蛋白家族预测方法可供学术研究人员通过电子邮件服务器进行测试。
We have trained a two-layered feed-forward neural network on a non-redundant data base of 130 protein chains to predict the secondary structure of water-soluble proteins. A new key aspect is the use of evolutionary information in the form of multiple sequence alignments that are used as input in place of single sequences. The inclusion of protein family information in this form increases the prediction accuracy by six to eight percentage points. A combination of three levels of networks results in an overall three-state accuracy of 70·8% for globular proteins (sustained performance). If four membrane protein chains are included in the evaluation, the overall accuracy drops to 70·2%. The prediction is well balanced between α-helix, β-strand and loop: 65% of the observed strand residues are predicted correctly. The accuray in predicting the content of three secondary structure types is comparable to that of circular dichroism spectroscopy. The performance accuracy is verified by a sevenfold cross-validation test, and an additional test on 26 recently solved proteins. Of particular practical importance is the definition of a position-specific reliability index. For half of the residues predicted with a high level of reliability the overall accuracy increases to better than 82%. A further strength of the method is the more realistic prediction of segment length. The protein family prediction method is available for testing by academic researchersviaan electronic mail server.