We present a comprehensive computational analysis of the single point mutational landscapes of the Monkeypox virus (MPXV) proteome. We reconstructed full single-point mutational landscapes of 171 MPXV proteins using two advanced mutational effect predictors, ESCOTT and iGEMME, selected for their superior performance on viral proteins, assessed by benchmarking against the experimental data in the ProteinGym (v1.0.0) dataset. A recent MPXV strain sequenced in July 2024 was used as the reference genome. Multiple sequence alignments and protein structures were generated using Colabfold v1.5.5, and the predicted structures were evaluated with pLDDT metric, secondary structure predictions, and comparisons with available experimental data, ensuring high confidence in the structural models. To prioritize the most mutation-sensitive proteins within the large MPXV proteome as prime candidates for drug or vaccine development, we introduced a novel, interpretable metric: Average Gene Mutation Sensitivity (AGMS). Among the top 20 identified proteins, several were membrane-associated proteins, expected to be important for viral interactions with the hosts. This analysis provides a valuable resource for assessing the impact of new MPXV variants and guiding therapeutic strategies. This pioneering study underscores the significance of understanding MPXV evolution in the context of the ongoing global health crisis and offers a robust computational framework to support this effort.