Home About us Contact
|
Home About us Contact | ||
|
|
||
Retention Coefficients (retention + coefficient)
Selected AbstractsApplication of hydrophilic interaction chromatography retention coefficients for predicting peptide elution with TFA and methanesulfonic acid ion-pairing reagentsJOURNAL OF SEPARATION SCIENCE, JSS, Issue 6-7 2010Chad E. Wujcik Abstract Hydrophilic retention coefficients for 17 peptides were calculated based on retention coefficients previously published for TSKgel silica-60 and were compared with the experimental elution profile on a Waters Atlantis HILIC silica column using TFA and methanesulfonic acid (MSA) as ion-pairing reagents. Relative peptide retention could be accurately determined with both counter-ions. Peptide retention and chromatographic behavior were influenced by the percent acid modifier used with increases in both retention and peak symmetry observed at increasing modifier concentrations. The enhancement of net peptide polarity through MSA pairing shifted retention out by nearly five-fold for the earliest eluting peptide, compared with TFA. Despite improvements in retention and efficiency (Neff) for MSA over TFA, a consistent reduction in calculated selectivity (,) was observed. This result is believed to be attributed to the stronger polar contribution of MSA masking and diminishing the underlying influence of the amino acid residues of each associated peptide. Finally, post-column infusion of propionic acid and acetic acid was evaluated for their potential to recover signal intensity for TFA and MSA counter-ions for LC-ESI-MS applications. Acetic acid generally yielded more substantial signal improvements over propionic acid on the TFA system while minimal benefits and some further reductions were noted with MSA. [source] Predictions of peptides' retention times in reversed-phase liquid chromatography as a new supportive tool to improve protein identification in proteomicsPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 4 2009Tomasz B, czek Dr. Abstract One of the initial steps of proteomic analysis is peptide separation. However, little information from RP-HPLC, employed for peptides separation, is utilized in proteomics. Meanwhile, prediction of the retention time for a given peptide, combined with routine MS/MS data analysis, could help to improve the confidence of peptide identifications. Recently, a number of models has been proposed to characterize quantitatively the structure of a peptide and to predict its gradient RP-HPLC retention at given separation conditions. The chromatographic behavior of peptides has usually been related to their amino acid composition. However, different values of retention coefficients of the same amino acid in different peptides at different neighborhoods were commonly observed. Therefore, specific retention coefficients were derived by regression analysis or by artificial neural networks (ANNs) with the use of a set of peptides retention. In the review, various approaches for peptide elution time prediction in RP-HPLC are presented and critically discussed. The contribution of sequence dependent parameters (e.g., amphipathicity or peptide sequence) and peptide physicochemical descriptors (e.g., hydrophobicity or peptide length) that have been shown to affect the peptide retention time in LC are considered and analyzed. The predictive capability of the retention time prediction models based on quantitative structure,retention relationships (QSRRs) are discussed in details. Advantages and limitations of various retention prediction strategies are identified. It is concluded that proper processing of chromatographic data by statistical learning techniques can result in information of direct use for proteomics, which is otherwise wasted. [source] | ||||||||||