Comprehensive Mutational Landscape Analysis of Monkeypox Virus Proteome

In this study, 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 an advanced mutational effect predictor, ESCOTT, selected for its superior performance on viral proteins. ESCOTT performance was assessed by benchmarking against the experimental data in the ProteinGym (v1.0.0) dataset that contains 48917 multiple and 173502 single point mutations. 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. We determined mutational sensitivity of all positions in a protein utilizing ESCOTT scores and demonstrated their functional implications on cysteine proteinase and helicase of MPXV. Moreover, we created an interactive visualization tool to visualize mutational landscapes and sensitivities in a publicly available Google Colab. Furthermore, we introduced a novel, interpretable metric (Average Gene Mutation Sensitivity) to prioritize the most mutation-sensitive proteins within the large MPXV proteome as prime candidates for drug or vaccine development. Among the top 20 proteins identified with this metric, several were membrane-associated proteins, proven to be important for viral interactions with the hosts in other viruses. This analysis provides a valuable resource for assessing the impact of new MPXV variants. 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.