高等学校化学学报

高等学校化学学报 ›› 2012, Vol. 33 ›› Issue (11): 2526.doi: 10.7503/cjcu20120119

• 物理化学 • 上一篇    下一篇

多肽一级结构表征与抗菌肽QSAM建模

苏满秀1,2, 王立峰1,2, 代志军1,2, 袁哲明1,2, 柏连阳2   

  1. 1. 湖南农业大学湖南省作物种质创新与资源利用重点实验室, 长沙 410128;
    2. 湖南农业大学生物安全科学技术学院, 长沙 410128
  • 收稿日期:2012-02-13 出版日期:2012-11-10 发布日期:2012-10-15
  • 通讯作者: 袁哲明,男,博士,教授,博士生导师,主要从事生物信息学研究.E-mail:zhmyuan@sina.com;柏连阳,男,博士,教授,博士生导师,主要从事农药学研究.E-mail:bailianyang2005@yahoo.com.cn E-mail:zhmyuan@sina.com;bailianyang2005@yahoo.com.cn
  • 基金资助:

    湖南省杰出青年科学基金(批准号: 10JJ1005);公益性行业(农业)科研专项基金(批准号: 201303029-8)和湖南省2011年财政厅项目(批准号: 62020411074)资助.

Primary Structural Characterizations of Polypeptide and Antimicrobial Peptides QSAM Modeling

SU Man-Xiu1,2, WANG Li-Feng1,2, DAI Zhi-Jun1,2, YUAN Zhe-Ming1,2, BAI Lian-Yang2   

  1. 1. Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization, Hunan Agricultural University, Changsha 410128, China;
    2. College of Bio-safety Science and Technology, Hunan Agricultural University, Changsha 410128, China
  • Received:2012-02-13 Online:2012-11-10 Published:2012-10-15

PDF (PC)

被引次数

4

摘要：

从整体上考虑多肽一级结构, 提出了3种仅基于多肽氨基酸序列、 计算简便、 适于不等长肽和可捕获多肽上下文关联特征的多肽新描述子, 即地统计学关联(GS-AA531)描述子、多尺度组分与关联(MSCC)描述子和地统计学关联与多尺度组分(GS-AA531-MSC)描述子. 将其应用于2个抗菌肽体系(等长肽与不等长肽)的结构表征, 并以支持向量回归建立QSAM模型. 模型的拟合、 留一法及独立测试结果表明, 结合特征筛选的新描述子GS-AA531与GS-AA531-MSC的预测精度明显稳定且优于其它参比描述子, 在多肽QSAM研究中具有广泛应用前景.

关键词: 结构表征, 定量序效模型, 抗菌肽, 支持向量回归, 特征筛选

Abstract:

Primary structure characterization is the key to quantitative sequence-activity modeling(QSAM) of polypeptides. This paper reported three new descriptors, GS-AA531, MSCC and GS-AA531-MSC, identified through integrating the information in peptide or protein primary structure. The calculations identified these descriptions were simple, only based on amino acid sequence, suitable to peptides with different lengths and could capture the context features. The new descriptors and other reference descriptors were applied to the two AMPs systems(equal and unequal length peptides) for constructing QSAM models combined with features screening. The accuracies of fitting, leave-one-out cross validation, and extra-sample prediction for the models based on GS-AA531 and GS-AA531-MSC descriptors improved significantly compared with those based on the other descriptors. Therefore, the new peptide or protein descriptors GS-AA531 and GS-AA531-MSC are pro-mising for broad applications in peptide or protein QSAM study.

Key words: Structural characterization, Quantitative sequence-activity model, Antimicrobial peptide, Support vector regression, Feature screening

中图分类号: 

TrendMD: