Citation: LIANG Gui-Zhao, LI Zhi-Liang, ZHOU Yuan, He Liu, ZHOU Peng. A New Peptide Sequences Representation Technique and Support Vector Machine for Quantitative Structure-Retention Modeling of Peptides in HPLC[J]. Acta Physico-Chimica Sinica, doi: 10.3866/PKU.WHXB20060903
一种新多肽表征方法及支持向量机用于肽HPLC定量结构-保留建模预测
从20种天然氨基酸的1369种性质参数经主成分分析得出一种新多肽序列表征方法——SZOTT. 将其用于71个不同长度肽序列表征, 以偏最小二乘(PLS)和支持向量机(SVM)建立定量结构-保留模型(QSRM). 研究表明, SZOTT能够较好表征71个肽序列特征, 其含信息量大且易操作, 与PLS相比, SVM对lgk建模预测表现出较强的拟合能力和良好外部预测能力, SZOTT表征方法和SVM建模可进一步用于肽HPLC保留行为研究.
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关键词:
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肽;SVM;QSRM;SZOTT
English
A New Peptide Sequences Representation Technique and Support Vector Machine for Quantitative Structure-Retention Modeling of Peptides in HPLC
A new representation technique for peptide sequences, namely SZOTT(scores vector of zero dimension, one dimension, two dimension, and three dimension), was derived from 1369 parameters of 20 coded amino acids using principle components analysis (PCA). It was then employed to express 71 peptide sequences with different lengths. Quantitative structure-retention modelings (QSRMs) were constructed by support vector machine (SVM) and partial least square (PLS). The results indicated that 71 peptide sequences could be preferably represented by SZOTT with many advantages, such as plentiful structural information and easy manipulation. Also simulative power for interior samples and predictive power for exterior samples by SVM were superior to those from PLS. SZOTT and SVM can be applied to develop QSRMs.
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Key words:
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Peptides;SVM;QSRM;SZOTT
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