Rescoring MS Amanda identification results with MS 2 Rescore MS 2 Rescore v3.0.0b4 was used for rescoring the MS Amanda identification results. We utilized the "TMT" MS 2 PIP model for fragment ion intensity predictions, retraining of DeepLC with transfer learning was enabled, and Mokapot with all default parameters was used as the rescoring engine. To overcome the very low number of positive examples for effective DeepLC transfer learning, the calibration_set_size and n_epochs DeepLC parameters were set to 2.000 and 250, respectively. This ensures that only confident PSMs are used to retrain the retention time prediction model. Similarly, due to the low number of confident PSMs per MS run in comparison to bulk proteomics, all PSM files for the same acquisition method were combined in a single rescoring run with MS 2 Rescore to provide Mokapot with a larger set of training examples. Each of the MS Amanda identification result file names were specified under the psm_file parameter, and the psm_file_type was set to "msamanda".