共 70 条
Quantitative structure - retention relationships of amino acids on the amino acid- and peptide-silica stationary phases for liquid chromatography
被引:10
作者:

Skoczylas, Magdalena
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Nicolaus Copernicus Univ Torun, Fac Chem, Chair Environm Chem & Bioanalyt, Gagarina 7 St, PL-87100 Torun, Poland Nicolaus Copernicus Univ Torun, Fac Chem, Chair Environm Chem & Bioanalyt, Gagarina 7 St, PL-87100 Torun, Poland

Bocian, Szymon
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Nicolaus Copernicus Univ Torun, Fac Chem, Chair Environm Chem & Bioanalyt, Gagarina 7 St, PL-87100 Torun, Poland Nicolaus Copernicus Univ Torun, Fac Chem, Chair Environm Chem & Bioanalyt, Gagarina 7 St, PL-87100 Torun, Poland

Buszewski, Boguslaw
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Nicolaus Copernicus Univ Torun, Fac Chem, Chair Environm Chem & Bioanalyt, Gagarina 7 St, PL-87100 Torun, Poland Nicolaus Copernicus Univ Torun, Fac Chem, Chair Environm Chem & Bioanalyt, Gagarina 7 St, PL-87100 Torun, Poland
机构:
[1] Nicolaus Copernicus Univ Torun, Fac Chem, Chair Environm Chem & Bioanalyt, Gagarina 7 St, PL-87100 Torun, Poland
关键词:
Amino acids;
Retention mechanism;
Structural similarity;
HPLC;
ELSD;
Stationary phases;
SOLVATOCHROMIC COMPARISON METHOD;
MULTIPLE LINEAR-REGRESSION;
MOLECULAR-MECHANISM;
DIOL COLUMN;
SEPARATION;
PREDICTION;
SOLVENT;
DESCRIPTORS;
SELECTIVITY;
BEHAVIOR;
D O I:
10.1016/j.chroma.2019.460514
中图分类号:
Q5 [生物化学];
学科分类号:
071010 ;
081704 ;
摘要:
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-FluorenylmethoxycarbonyI)O-tert-buty-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. (C) 2019 Published by Elsevier B.V.
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