以88%的准确率预测蛋白质中半胱氨酸的二硫键结合状态。
Prediction of the disulfide-bonding state of cysteines in proteins at 88% accuracy.
作者信息
Martelli Pier Luigi, Fariselli Piero, Malaguti Luca, Casadio Rita
机构信息
Laboratory of Biocomputing, CIRB/Department of Biology, University of Bologna, 40126 Bologna, Italy.
出版信息
Protein Sci. 2002 Nov;11(11):2735-9. doi: 10.1110/ps.0219602.
Abstract
The task of predicting the cysteine-bonding state in proteins starting from the residue chain is addressed by implementing a new hybrid system that combines a neural network and a hidden Markov model (hidden neural network). Training is performed using 4136 cysteine-containing segments extracted from 969 nonhomologous proteins of well-resolved three-dimensional structure. After a 20-fold cross-validation procedure, the efficiency of the prediction scores as high as 88% and 84%, when measured on cysteine and protein basis, respectively. These results outperform previously described methods for the same task.
摘要
通过实施一种结合神经网络和隐马尔可夫模型的新型混合系统（隐藏神经网络），解决了从残基链开始预测蛋白质中半胱氨酸结合状态的任务。使用从969个具有良好解析三维结构的非同源蛋白质中提取的4136个含半胱氨酸片段进行训练。经过20倍交叉验证程序后，分别以半胱氨酸和蛋白质为基础进行测量时，预测效率高达88%和84%。这些结果优于先前针对同一任务所描述的方法。
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