LVQ神经网络方法预测蛋白质结构中的二硫键

在蛋白质结构预测的研究中,一个重要的问题就是正确预测二硫键的连接,二硫键的准确预测可以减少蛋白质构像的搜索空间,有利于蛋白质的3D结构的预测,成功地将LVQ神经网络方法引入蛋白质的二硫键的预测工作中。结果表明蛋白质的二硫键的连接与半胱氨酸的局域序列模式有重要联系,可以由蛋白质的一级结构序列预测该蛋白质的二硫键的连接方式,应用这个方法对蛋白质结构的二硫键进行了预测取得了良好的结果。

Prediction of Disulfide Bonding in Protein Structure Based on LVQ Neural Network Method

An important problem in protein structure prediction is the correct location of disulfide bonding in proteins. The location of disulfide bonding can strongly reduce the search in the conformational space of protein structure. Therefore the correct prediction of the disulfide bonding starting from the protein residue sequence may also help in predicting its 3D structure. The LVQ artificial neural network method was applied to predict the disulfide bonding of protein structure. We find that the local sequence arrangement of cysteine is of great significance to the disulfide bonding. Therefore the disulfide bonding can be predicted by its primary structure. This method is used to predict disulfide bonding in protein structure and a fine result is got.