Disulfide bond prediction integrated protein secondary structure information
结合蛋白质二级结构信息预测蛋白质空间结构中的二硫键

n protein-folding prediction, the location of disulfide bonds can strongly reduce the search in the conformational space. In order to improve prediction accuracy, analyzed the bias of free cysteines and cystines in the secondary structure preference, and proposed adding protein secondary structure information to the inputs of BP neural network. Selected 252 proteins from the SWISS-PROT database, and divided them into 4 even subsets. From the results of 4-fold cross validation, found that integrating protein secondary structure information can improve prediction accuracy. The results show that this encoding is feasible and effective.