BO has recently emerged as an attractive tool for the optimal design of experiments, particularly for peptide optimization tasks. In this work, we developed a general BO method and systematically benchmarked it by varying different parameters, conditions, and initialization strategies (sequence-or structure-based methods). The method was benchmarked on the human MHC class I protein dataset. MHC class I is the most studied protein-peptide complex due to its crucial role in the adaptive immune system by capturing peptide antigens for display on cell surfaces. The vast amount of data available enabled the derivation of SMM PMBEC scoring matrices, which can be used as a digital twin to reliably query the pIC50 of any arbitrary peptide sequences (8-mers to 11-mers) binding to MHC I without requiring additional experiments. This enables the emulation of a virtual DMT platform for testing and benchmarking BO.