Accurate prediction of protein secondary structural content
Accurate prediction of protein secondary structural content
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DOI：
10.1023/a:1010967008838
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发表时间：
2001-04-01
期刊：
影响因子：
--
通讯作者：
Pan, XM
中科院分区：
文献类型：
--
作者：
Lin, Z;Pan, XM
提出了一种基于蛋白质一级序列的改进多元线性回归（MLR）预测蛋白质二级结构含量的方法。考虑了氨基酸组成、侧链质量的自相关函数和相互作用函数。对704种不相关蛋白进行叠刀试验预测的平均绝对误差分别为0.088、0.081和0.059，α -helix、β -sheet和coil的标准差分别为0.073、0.066和0.055。引入预测二级结构含量之和应接近1.0作为评价预测是否可接受的标准。虽然只有预测二级结构含量总和在0.99 ~ 1.01之间的预测被接受（约占所有蛋白质的11%），但alpha -helix的绝对误差为0.058，beta -sheet的绝对误差为0.054，coil的绝对误差为0.045。
An improved multiple linear regression (MLR) method is proposed to predict a protein's secondary structural content based on its primary sequence. The amino acid composition, the autocorrelation function, and the interaction function of side-chain mass derived from the primary sequence are taken into account. The average absolute errors of prediction over 704 unrelated proteins with the jackknife test are 0.088, 0.081, and 0.059 with standard deviations 0.073, 0.066, and 0.055 for alpha -helix, beta -sheet, and coil, respectively. That the sum of predicted secondary structure content should be close to 1.0 was introduced as a criterion to evaluate whether the prediction is acceptable. While only the predictions with the sum of predicted secondary structure content between 0.99 and 1.01 are accepted (about 11% of all proteins), the absolute errors are 0.058 for alpha -helix, 0.054 for beta -sheet, and 0.045 for coil.