Using Support Vector Machine Regression to Model the Retention of Peptides in Immobilized Metal-affinity Chromatography.
Using Support Vector Machine Regression to Model the Retention of Peptides in Immobilized Metal-affinity Chromatography.
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DOI：
10.1016/j.snb.2007.02.004
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发表时间：
2007-07
期刊：
影响因子：
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通讯作者：
B. Kermani;Igor A. Kozlov;Peter C. Melnyk;Chanfeng Zhao;John P. Hachmann;David L. Barker;Michal Lebl
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文献类型：
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作者：
B. Kermani;Igor A. Kozlov;Peter C. Melnyk;Chanfeng Zhao;John P. Hachmann;David L. Barker;Michal Lebl
用数百种模型肽研究了固定化金属亲和色谱法（IMAC）中含组氨酸肽的保留。镍柱中的保留主要是由组氨酸残基的数量决定的；然而，肽的氨基酸组成也起着重要的作用。采用基于支持向量机的回归模型学习并预测镍柱上氨基酸组成与停留时间之间的关系。该模型主要由组氨酸残基的计数和肽的等电点控制。
Retention of histidine-containing peptides in immobilized metal-affinity chromatography (IMAC) has been studied using several hundred model peptides. Retention in a Nickel column is primarily driven by the number of histidine residues; however, the amino acid composition of the peptide also plays a significant role. A regression model based on support vector machines was used to learn and subsequently predict the relationship between the amino acid composition and the retention time on a Nickel column. The model was predominantly governed by the count of the histidine residues, and the isoelectric point of the peptide.