Prediction models are used instead of ground truth values, as it would be infeasible to obtain reference measurements for all potential candidate peptides, especially in an immunopeptidomics setting. Evidently, the closer the predictions are to the ground truth, the more relevant the features will be. For example, MHCquant tested ΔRT and concluded that it was not an effective feature. In contrast, for DeepRescore ΔRT was consistently one of the features with the highest weight. A possible explanation for this apparent contradictory conclusion is that the RTPredict tool MHCquant used was published more than 15 years ago and might provide less accurate predictions compared to more recent alternative retention time prediction models. Hence, this illustrates that highly accurate predicted peptide properties are needed to provide informative features for PSM rescoring.