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氨基酸和肽-硅胶固定相上氨基酸的定量结构-保留关系在液相色谱中。

Quantitative structure - retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography.

机构信息

Chair of Environmental Chemistry & Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Gagarina 7 St., 87-100 Torun, Poland.

Chair of Environmental Chemistry & Bioanalytics, Faculty of Chemistry, Nicolaus Copernicus University in Toruń, Gagarina 7 St., 87-100 Torun, Poland.

出版信息

J Chromatogr A. 2020 Jan 4;1609:460514. doi: 10.1016/j.chroma.2019.460514. Epub 2019 Sep 4.

Abstract

Quantitative structure - retention relationships analysis was applied to investigate the molecular retention mechanism of proteinogenic and non-proteinogenic amino acids on the amino acid- and peptide-silica stationary phases. Twelve stationary phases with chemically bonded amino acids of different types (glycine, alanine, phenylalanine, leucine, methionine, aspartic acid, and N-(9-Fluorenylmethoxycarbonyl)-O-tert-butyl-l-tyrosine) and chains lengths (amino acid, dipeptide, and tripeptide) were tested. In order to compare chromatographic properties of the prepared materials with the conventional columns, the amino-bonded phases (laboratory-prepared and commercial one) were also studied. For each of analyte, the molecular descriptors were calculated using quantum mechanics method. The QSRR models were determined using 13 molecular descriptors mainly related to the surface area, hydrophobicity, polarity, ion-exchange and hydrogen bonding capabilities of the analytes. Finally, the prediction potency of the molecular modeling descriptors-based models was also independently studied for the tested stationary phases using 15 training set and 6 test set of amino acids.

摘要

定量构效关系分析被应用于研究蛋白质和非蛋白质氨基酸在氨基酸和肽硅胶固定相上的分子保留机制。测试了 12 种具有不同类型(甘氨酸、丙氨酸、苯丙氨酸、亮氨酸、甲硫氨酸、天冬氨酸和 N-(9-芴甲氧羰基)-O-叔丁基-L-酪氨酸)和链长(氨基酸、二肽和三肽)的化学键合氨基酸的固定相。为了将制备材料的色谱性能与常规柱进行比较,还研究了氨基键合相(实验室制备的和商业的)。对于每种分析物,使用量子力学方法计算分子描述符。使用主要与分析物的表面积、疏水性、极性、离子交换和氢键能力相关的 13 个分子描述符确定 QSRR 模型。最后,还使用 15 个训练集和 6 个测试集的氨基酸,独立研究了基于分子建模描述符的模型对测试固定相的预测能力。

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