期刊论文

Yuan Zhe-Ming;Tan Si-Qiao

中文

生物化学与生物物理进展

1000-3282

2016

43

7

691-698

10.16476/j.pibb.2016.0002

Specialized Research Fund for the Doctoral Program of Higher EducationSpecialized Research Fund for the Doctoral Program of Higher Education (SRFDP) [20124320110002]; Natural Science Foundation of Hunan Province, ChinaNatural Science Foundation of Hunan Province [14JJ2082]; Science and Technology Planning Projects of Changsha, China [K1406018-21]

本校为第一且通讯机构

糖基化是蛋白质翻译后的主要修饰,O-糖基化的固定模式未知,高精度识别O-糖基化位点是机器学习面临的挑战性问题.以迄今最大的人O-糖基化位点Steentoft数据集为基础,本文首次提出了基于位置的卡方差表特征χ~2-pos,融合伪氨基酸序列进化信息PsePSSM以及无方向的k间隔氨基酸对组分Undirected-CKSAAP表征序列,构建5个正负样本均衡的支持向量机分类器,经加权投票,独立测试准确率、Matthew相关系数及ROC曲线下面积,分别达到了89.62%、0.79、0.96,明显优于文献报道结果.χ~2-pos、PsePSSM与Undirected-CKSAAP三种特征的融合在蛋白质糖基化、磷酸化等位点预测中有广泛应用前景.

Glycosylation is a major modification process in post -translational modification of protein. Accurate prediction of 0-linked glycosylation sites is a big challenging faced by machine -learning, for the fixed -model of 0-linked glycosylation is not yet known. In this paper, on the basis of the largest-ever Steentoft database up to now, a new feature chi-square score difference table method based on position (y2-pos) was first proposed, which combined with pseudo position -specific scoring matrix (PsePSSM) and undirected composition of k-spaced amino acid pairs (Undirected-CKSAAP) were used to ...