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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 the same. The RP-amide column, combined with a formic acid-acetonitrile based gradient system, performed as best.A- Quantitative structure-retention relationship (QSRR) models were constructed for 16 different chromatographic systems. The accuracy of this peptide retention model (QSRR) models were constructed for 5 variables: log SumAA, log Sv, clog P, log nHDon and log nHAcc. was demonstrated by the comparison between predicted and experimentally obtained retention times, explaining on average 86% of the variability. More over, using an external set of 5 validation peptides, the predict ive power of the model was also seen. 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.