CAJ | 학술논문

采用改进的氨基酸组成、SARAH1疏水尺度值、改进的二肽频率特征、间隔氨基酸对组成特征、蛋白质物理化学性质的自相关函数特征值表征给定的蛋白质序列段,然后用小波频谱来提取特征参数值,用支持向量机来预测棕榈酰化位点.模型查准率为0.880,查全率为0.859,F值为0.869,ROC曲线的面积为0.87.研究结果表明,使用多特征预测蛋白质棕榈化位点方法达到了现有预测算法的水平,能够较准确地预测蛋白质棕榈化位点.
채용개진적안기산조성、SARAH1소수척도치、개진적이태빈솔특정、간격안기산대조성특정、단백질물이화학성질적자상관함수특정치표정급정적단백질서렬단,연후용소파빈보래제취특정삼수치,용지지향량궤래예측종려선화위점.모형사준솔위0.880,사전솔위0.859,F치위0.869,ROC곡선적면적위0.87.연구결과표명,사용다특정예측단백질종려화위점방법체도료현유예측산법적수평,능구교준학지예측단백질종려화위점.
Palmitoylation is an important post-translational modification, which participates many cellu-lar processes, including antigen processing, DNA transcription and repair, apoptosis, immune reaction and in-flammation, regulating cell surface receptors, ion channels and secretor pathway, nerve and muscle degenera-tion, viral infections and so on.Hence, the accurate prediction of palmitoylation sites can be of help in under-standing the molecular mechanism of palmitoylation and also in designing various related experiments.Here we present an accurate method to identify palmitoylation sites from protein sequence information using a support vector machine model.It has achieved an accuracy of 88%, which shows that this method will be a useful tool to find palmitoylation sites in a protein.