用一种新的信息离散性度量方法,即Function of Degree of Disagreement(FDOD),从蛋白质原始序列出发区分同源二聚体、同源三聚体、同源四聚体和同源六聚体.该方法用蛋白质原始序列的子序列分布来描述氨基酸序列,从而充分考虑了蛋白质序...用一种新的信息离散性度量方法,即Function of Degree of Disagreement(FDOD),从蛋白质原始序列出发区分同源二聚体、同源三聚体、同源四聚体和同源六聚体.该方法用蛋白质原始序列的子序列分布来描述氨基酸序列,从而充分考虑了蛋白质序列的信息.随着子序列长度的增加,两个数据集上自检验和jack-knife检验的各个分类指标都有快速增加的趋势,实验表明残基顺序对同源寡聚蛋白质的识别起重要作用,FDOD方法是同源寡聚蛋白质分类的简单而有效的工具.这也进一步证实了蛋白质原始序列包含着四级结构信息.展开更多
Protein homo-oligomers play an important role in various vital activities. Successful prediction of protein homo-oligomers directly from primary sequence is very beneficial to understand their protein function. In thi...Protein homo-oligomers play an important role in various vital activities. Successful prediction of protein homo-oligomers directly from primary sequence is very beneficial to understand their protein function. In this paper, a total of 14 238 homo-oligomeric protein sequences are predicted by IB1 algorithm. 10-fold cross-validation test is applied to test the predictive capability of the proposed method. The predictive results show that overall prediction accuracy is 90.46%, which is at least 9% higher than that of previous results; furthermore,the sensitivity and Matthew＇s correlation coefficient for each class of homo-oligomers are also improved significantly. The results show that IB1 algorithm is effective and feasible,and very suitable for predicting protein homo-oligomer types.展开更多
文摘用一种新的信息离散性度量方法,即Function of Degree of Disagreement(FDOD),从蛋白质原始序列出发区分同源二聚体、同源三聚体、同源四聚体和同源六聚体.该方法用蛋白质原始序列的子序列分布来描述氨基酸序列,从而充分考虑了蛋白质序列的信息.随着子序列长度的增加,两个数据集上自检验和jack-knife检验的各个分类指标都有快速增加的趋势,实验表明残基顺序对同源寡聚蛋白质的识别起重要作用,FDOD方法是同源寡聚蛋白质分类的简单而有效的工具.这也进一步证实了蛋白质原始序列包含着四级结构信息.
基金Supported by the Discipline-Crossing Research Foundation of Huazhong Agricultural University (2008XKJC006)
文摘Protein homo-oligomers play an important role in various vital activities. Successful prediction of protein homo-oligomers directly from primary sequence is very beneficial to understand their protein function. In this paper, a total of 14 238 homo-oligomeric protein sequences are predicted by IB1 algorithm. 10-fold cross-validation test is applied to test the predictive capability of the proposed method. The predictive results show that overall prediction accuracy is 90.46%, which is at least 9% higher than that of previous results; furthermore,the sensitivity and Matthew＇s correlation coefficient for each class of homo-oligomers are also improved significantly. The results show that IB1 algorithm is effective and feasible,and very suitable for predicting protein homo-oligomer types.
