کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1211047 | 965463 | 2007 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparative evaluation of high-performance liquid chromatography stationary phases used for the separation of peptides in terms of quantitative structure-retention relationships
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
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چکیده انگلیسی
Chromatographic measurements were made on 17 physicochemically diversified high-performance liquid chromatography (HPLC) columns which were further analyzed in terms of their similarities and dissimilarities for 25 carefully designed, structurally diverse peptides showing distinctly distinguished groups. The goal of the study was to investigate the molecular mechanism of retention and to find an objective manner of quantitative comparison of retention properties and classification of modern stationary phases for reverse phase HPLC (RP-HPLC). We utilized the structural descriptors of peptides obtained from molecular modeling to describe their chromatographic retention behavior under given HPLC conditions. Quantitative structure-retention relationships (QSRR) with the following descriptors were employed: logarithm of the sum of amino acid retention contributions in a given peptide, log SumAA, logarithm of the Van der Waals volume of the peptide, log VDWvol, and logarithm of its calculated n-octanol/water coefficient, clog P. The best QSRR equations were obtained in the case of monolithic and regular octadecylsilica columns. On the other hand, the combination of QSRR and principal component analysis (PCA) can be considered as the efficient tool allowing column classification and searching for orthogonal HPLC conditions required to separate peptides.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Chromatography A - Volume 1175, Issue 1, 14 December 2007, Pages 49-54
Journal: Journal of Chromatography A - Volume 1175, Issue 1, 14 December 2007, Pages 49-54
نویسندگان
Monika Michel, Tomasz BÄ
czek, Sylwia StudziÅska, Karolina Bodzioch, Tobias Jonsson, Roman Kaliszan, BogusÅaw Buszewski,