The molecular descriptor log SumAA and its alternatives in QSRR models to predict the retention of peptides

The use of the experimental molecular descriptor log SumAA and some possible alternatives were evaluated in the QSRR analysis of peptides. To quantitatively characterize the structure of analytes in a previously proposed QSRR the following three structural descriptors were applied: the logarithm of the sum of gradient retention times of the amino acids composing the individual peptide, log SumAA; the logarithm of the peptide’s van der Waals volume, log VDWVol; and the logarithm of its theoretically calculated n-octanol–water partition coefficient, clog P. Taking into consideration that most amino acids were hardly retained in the different RP-HPLC systems on which the peptides retention was measured, the contribution of most amino acids to the log SumAA descriptor is rather constant. Therefore, to enlarge the variability of the descriptor and the amino acids contributions for a given series of peptides, in a first instance, it was evaluated whether, by changing the chromatographic conditions, the retention differences between the amino acids could be increased, while maintaining their mutual selectivity. It was not evident to find such conditions. Secondly, it was also investigated whether the experimental descriptor log SumAA can be replaced by a theoretical, either based on a simple or on a weighted counting of the amino acids composing the peptide. The weighting factor for the retained amino acids was determined by their experimental gradient retention times measured on different systems. The predictive abilities of the new QSRR models (applying the alternative descriptors) were assessed using the leave-one-out cross-validation procedure and compared to that of the initial model. Finally, a descriptor was defined for which the retention measurement of only a limited number of amino acids is required. It resulted in QSRR models with similar predictive properties as those with log SumAA, but with a reduced workload.