Using support vector machine regression to model the retention of peptides in immobilized metal-affinity chromatography

被引：17

作者：

Kermani, B. G. ^{[1
]}

Kozlov, I. ^{[1
]}

Melnyk, P. ^{[1
]}

Zhao, C. ^{[1
]}

Hadimann, J. ^{[1
]}

Barker, D. ^{[1
]}

Lebl, M. ^{[1
]}

机构：

[1] Illumina Inc, San Diego, CA 92122 USA

来源：

SENSORS AND ACTUATORS B-CHEMICAL | 2007年 / 125卷 / 01期

关键词：

support vector machines; regression; peptide; metal-affinity chromatography; retention time;

D O I：

10.1016/j.snb.2007.02.004

中图分类号：

O65 [分析化学];

学科分类号：

070302 [分析化学]; 081704 [应用化学];

摘要：

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 govemed by the count of the histidine residues, and the isoelectric point of the peptide. (c) 2007 Elsevier B.V. All rights reserved.

引用

页码：149 / 157

页数：9

共 16 条

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