PREDICTION OF PROTEIN-FOLDING CLASS USING GLOBAL DESCRIPTION OF AMINO-ACID-SEQUENCE
PREDICTION OF PROTEIN-FOLDING CLASS USING GLOBAL DESCRIPTION OF AMINO-ACID-SEQUENCE
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DOI：
10.1073/pnas.92.19.8700
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发表时间：
1995-09-12
影响因子：
11.1
通讯作者：
KIM, SH
中科院分区：
文献类型：
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作者：
DUBCHAK, I;MUCHNIK, I;KIM, SH
提出了一种基于全局蛋白质链描述和投票过程的蛋白质折叠类预测方法。通过在由83个折叠类组成的数据库上训练的计算机模拟神经网络来实现最佳描述符的选择。蛋白质链描述符包括氨基酸属性的总体组成、转变和分布，例如相对疏水性、预测的二级结构和预测的溶剂暴露。测试表明，蛋白质被分配到正确的类别（正确的阳性预测），平均准确度为71.7%，而蛋白质不属于特定类别的反向预测（正确的阴性预测）准确度为90-95%。当对本研究中使用的254个结构进行测试时，前两个预测在91%的情况下包含正确的类。
We present a method for predicting protein folding class based on global protein chain description and a voting process. Selection of the best descriptors was achieved by a computer-simulated neural network trained on a data base consisting of 83 folding classes, Protein-chain descriptors include overall composition, transition, and distribution of amino acid attributes, such as relative hydrophobicity, predicted secondary structure, and predicted solvent exposure, Cross-validation testing was performed on 15 of the largest classes. The test shows that proteins were assigned to the correct class (correct positive prediction) with an average accuracy of 71.7%, whereas the inverse prediction of proteins as not belonging to a particular class (correct negative prediction) was 90-95% accurate. When tested on 254 structures used in this study, the top two predictions contained the correct class in 91% of the cases.