摘要
Different fused-core stationary phase chemistries（C18,Amide,Phenyl-hexyl and Peptide ES-C18） were used for the analysis of 21 structurally representative model peptides.In addition,the effects of the mobile phase composition（ACN or MeOH as organic modifier;formic acid or acetic acid,as acidifying component） on the column selectivity,peak shape and overall chromatographic performance were evaluated.The RP-amide column,combined with a formic acid-acetonitrile based gradient system,performed as best.A peptide reversed-phase retention model is proposed,consisting of 5 variables：log SumAA,log Sv,clog P,log nHDon and log nHAcc.Quantitative structure-retention relationship（QSRR） models were constructed for 16 different chromatographic systems.The accuracy of this peptide retention model was demonstrated by the comparison between predicted and experimentally obtained retention times,explaining on average 86% of the variability.Moreover,using an external set of 5 validation peptides,the predictive power of the model was also demonstrated.This peptide retention model includes the novel in-silico calculated amino acid descriptor,AA,which was calculated from log P,3D-MoRSE,RDF and WHIM descriptors.
Different fused-core stationary phase chemistries（C18,Amide,Phenyl-hexyl and Peptide ES-C18） were used for the analysis of 21 structurally representative model peptides.In addition,the effects of the mobile phase composition（ACN or MeOH as organic modifier;formic acid or acetic acid,as acidifying component） on the column selectivity,peak shape and overall chromatographic performance were evaluated.The RP-amide column,combined with a formic acid-acetonitrile based gradient system,performed as best.A peptide reversed-phase retention model is proposed,consisting of 5 variables：log SumAA,log Sv,clog P,log nHDon and log nHAcc.Quantitative structure-retention relationship（QSRR） models were constructed for 16 different chromatographic systems.The accuracy of this peptide retention model was demonstrated by the comparison between predicted and experimentally obtained retention times,explaining on average 86% of the variability.Moreover,using an external set of 5 validation peptides,the predictive power of the model was also demonstrated.This peptide retention model includes the novel in-silico calculated amino acid descriptor,AA,which was calculated from log P,3D-MoRSE,RDF and WHIM descriptors.
基金
funded by a Ph.D.grant of "Institute for the Promotion of Innovation through Science and Technology in Flanders(IWT-Vlaanderen)"(No.091241 for MD and 073402 for SVD)
the Special Research Fund of the Ghent University (Grant no.BOF 01J22510 for EW and BOF 01D38811 for SS)
