We have developed a sequence-specific model for predicting slopes (S) in the fundamental equation of linear solvent strength theory for the reversed-phase HPLC separation of tryptic peptides detected in a typical bottom-up-proteomics experiment. These slopes control the variation in the separation selectivity observed when the physical parameters of chromatographic separation, such as gradient slope, flow rate, and column size are altered. For example, with the use of an arbitrarily chosen set of tryptic peptides with a 4-times difference in the gradient slope between two experiments, the R(2)-value of correlation between the observed retention times of identical species decreases to ~0.993-0.996 (compared to a theoretical value of ~1.00). The observed retention time shifts associated with variations of the gradient slope can be predicted a priori using the approach described here. The proposed model is based on our findings for a set of synthetic species (Vu, H.; Spicer, V.; Gotfrid, A.; Krokhin, O. V. J. Chromatogr., A, 2010, 1217, 489-497), which postulate that slopes S can be predicted taking into account simultaneously peptide length, charge, and hydrophobicity. Here we extend this approach using an extensive set of real tryptic peptides. We developed the procedure to accurately measure S-values in nano-RP HPLC MS experiments and introduced sequence-specific corrections for a more accurate prediction of the slopes S. A correlation of ~0.95 R(2)-value between the predicted and experimental S-values was demonstrated. Predicting S-values and calculating the expected retention time shifts when the physical parameters of separation like gradient slope are altered will facilitate a more accurate application of peptide retention prediction protocols, aid in the transfer of scheduled MRM (SRM) procedures between LC systems, and increase the efficiency of interlaboratory data collection and comparison.