Evaluation of a generalized use of the log Sum(k+1)AA descriptor in a QSRR model to predict peptide retention on RPLC systems.

At the current state of knowledge, the rational optimization of the chromatographic separation of peptides, as well as the identification of proteins in proteomics are challenges for analytical chemists. In this paper the generalized applicability of a recently derived descriptor log Sum(k+1)AA in a QSRR equation to model peptide retention in RP-LC systems was evaluated. For that purpose, two sets of peptides analyzed on dissimilar RP-LC systems were considered. A first set of 28 peptides was measured on 17 columns/systems, while a second of 70 peptides was eluted on four. The aim of this work was to confirm the usefulness of the partly experimental log Sum(k+1)AA descriptor for the prediction of peptides retention compared to the initially applied, fully experimental log SumAA descriptor. The verification of the predictive abilities of both QSRR models, applying either the initial or the alternative descriptor, was done by using the leave-one-out and leave-three-out cross-validation procedures. The results seem to demonstrate that the QSRR model with log Sum(k+1)AA, for which the retention measurement of only seven out of 20 existing amino acids is necessary, possesses similar or in some cases even better predictive abilities than that containing log SumAA.