基于两层分类器的半胱氨酸氧化还原状态预测方法

提出了两层混合分类器来预测蛋白质半胱氨酸氧化还原状态,第一层总体线性分类器利用氨基酸百分含量作为输入信息,第二层局部SVM分类器利用半胱氨酸周围局部序列作为输入信息。以2002年4月份的PISCES culled PDB数据库中的 639条蛋白质多肽链作为研究对象,共含有584条二硫键,2 904个半胱氨酸。经严格的折叠刀方法检验,预测半胱氨酸的氧化还原状态准确率最高可达84．1％(半胱氨酸水平)和80．1％(蛋白质水平)。结果表明这种将蛋白质总体信息与局部上下文序列信息结合起来构建的两层混和分类器具有较高的预测准确率。研究结果也表明总体氨基酸百分含量和半胱氨酸周围局部序列都携带有二硫键形成的相关信息,暗示了半胱氨酸是否形成二硫键不但取决于蛋白质全局的结构信息同时也受到局部序列信息的影响。

A novel prediction method of the oxidization state of cysteines in proteins based on two-stage classifier

A novel approach has been proposed to predict the disulfide bonding state of cysteines in proteins by constructing a two-stage classifier combining a first global linear discriminator based on their amino acid composition and a second local support vector machine classifier. The present study was based on the PISCES Culled PDB with 639 protein polypeptide chains and 584 disulfide bonds in April 2002. The overall prediction accuracy of this hybrid classifier for the disulfide bonding state of cysteines in proteins has scored 84. 1% and 80. 1% , when measured on cysteine and protein basis using the rigorous jack-knife procedure, respectively. The result demonstrates the applicability of this novel method and provides comparable prediction performance compared with existing methods for the prediction of the oxidation states of cysteines in proteins. It also indicates that whether cysteines should form disulfide bonds depends not only on the global structural features of proteins but also on the local sequence environment of proteins.