摘要
以氨基酸的531个物理化学性质参数直接表征肽的结构,基于支持向量回归发展了一种新的高维特征非线性快速筛选方法,将其应用于苦味二肽和血管紧张素转化酶抑制剂2个肽体系的定量序效关系(QSAR)建模,各筛选获得10个意义明确的保留描述子.以保留描述子建立支持向量回归模型,其拟合精度、留一法交叉测试精度和外部预测精度较文献报道结果均有较大幅度提升,优势明显;对所建模型进行了非线性回归显著性测验、单因子相对重要性显著性测验和单因子效应分析,增强了模型的可解释性.新方法在肽、蛋白质QSAR建模等高维数据回归预测领域有广泛应用前景.
Each amino acid residue of one peptide was characterized directly by 531 physicochemical property parameters. Based on support vector regression （SVR） we developed a new nonlinear rapid feature selection method for high dimensional data, which was applied to a quantitative sequenca- activity relationship （QSAR） study of two peptide systems （bitter tasting thresholds and angiotensin converting enzyme inhibitors）. In both systems, 10 descriptors with clear meaning were reserved. We established a SVR model for both peptide systems using the reserved descriptors of the peptides. For both models the accuracies of fitting, the leave-one-out cross validation, and the external prediction improved significantly compared with the results reported in literature. To enhance the interpretability of the models, significance tests of the nonlinear regression model, single-factor relative importance, and a single-factor effect analysis were carried out. The new method has broad application prospects for regression forecasting of high dimensional data such as QSAR modeling of peptide or proteins.
出处
《物理化学学报》
SCIE
CAS
CSCD
北大核心
2011年第7期1654-1660,共7页
Acta Physico-Chimica Sinica
基金
湖南省杰出青年科学基金(10JJ1005)
高等学校博士点基金(200805370002)
湖南省2008年高校科技创新团队项目资助~~
关键词
高维特征:特征选择:肽:定量序效关系:支持向量机
High dimensional feature
Feature selection
Peptide
Quantitative sequence-activityrelationship
Support vector machine
