The results raise the question of whether structure-based methods can accelerate the optimization of peptide sequences. Success stories have been reported in the literature and experimentally validated, demonstrating the usefulness of computational tools . In our context, MHC class I complex structures will be used as input to predict the impact of peptide side-chain substitutions on the binding. Various methods were developed to predict changes in binding affinity upon mutation, coupling interaction energy scoring between complex partners and structural sampling to accommodate larger side chains . In this study, we used the Rosetta flex ∆∆G and FlexPepDock, and FoldX methods to scores the protein-peptide interactions upon mutations. Overall, results show that BO runs initiated with predictions from the structure-based methods do not outperform BO runs initiated with the homolog scanning strategy (Fig. and). In fact, predictions from structure-based methods can negatively affect BO performance, as seen for the 8-and 10-mers. This could be explained by the weak correlations observed between pIC50s from SMM PMBEC PSSMs and the predicted protein-peptide interaction scores (Table ). However, more accurate predictions do not guarantee superior performance over homolog scanning either, as seen with the 9-mers complexes. Correlations range from 0.5 to 0.72 using the Rosetta Flex ∆∆G method. Furthermore, BO runs initiated with the more computationally intensive Rosetta Flex ∆∆G method do not give an advantage over cheaper methods, such as Rosetta FlexPepDock and FoldX. Further refinement of the structure-based methods tested here might be needed to improve mutation prediction accuracy. For instance, applying filters that consider shape complementarity or the number of buried unsatisfied hydrogen bonds could help filtering out non-binding peptide sequences .