To demonstrate that the method is not restricted to collagen-like systems only but shows transferability, we validated the performance on a different protein, the F0F1 domain of FERM. It exhibits a vastly different composition of aminoacids, i.e., of chemical environments. It also is structurally diverse, consisting of two folded parts and one intrinsically disordered region (Figure ). The simulations were kindly provided by Buhr et al. System preparation was done analogous to training data generation, as detailed in the methods section. For predicting the barriers, the ensemble model trained on collagen trajectories and synthetic systems was used. As shown in Figure D, the model does not perform as good as on its training target collagen, but still achieves an MAE of 4.6 ± 4.8 kcal/mol. We note that collagen only consists of triple helices and the model has not seen other secondary structures, let alone intrinsically disordered proteins. On top, the amino acid distribution varies significantly between the training data set and the testing data used here (Figure ). Therefore, this is close to the worst case scenario, and yet the model still delivers useful results. More importantly, starting from the pretrained model, the performance can be improved at a low cost in a transfer learning scheme as outlined earlier. Using only 500 new training structures from the FERM F0F1 domain, the MAE decreases already to 3.7 ± 4.2 kcal/mol (Figure ).