Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.
The manuscript mainly focuses on evaluating the affinity of FDA-approved, non-FDA, and DrugBank against the main targets related to Monkeypox disease, namely A48R, D13L, F13L, and I7L. The structures of the A48R and D13L targets have their 3D structures resolved, but the others were predicted using the AlphaFold2 server. All molecular docking simulations with the screened compounds were performed directly from the structures obtained from the crystals (A48R and D13L) and by modeling (A50R, F13L, and I7L), and the stability of the selected compounds was then evaluated through simulations of molecular dynamics of complexes.
The work has its merits for approaching a topic of relevance and applicability. However, some areas of the work that deserve attention were not properly explored.
The authors did not describe how many drugs were screened.
The temperature used for MD (303.15 K/30 degrees Celsius) is not explained. In general, it is used at the physiological temperature (310.15 K/37 o Celsius). Moreover, the protonation state of proteins was not predicted for any pH (also, in general, it is used for the physiological pH).
The authors did not say MD time simulation, which is a basic piece of information regarding MD parameters.
The docking should be performed from frames obtained from MD simulations. The idea is to use the entire trajectory. However, authors could cluster the structures focused only on the binding site to extract the most important frames. Both strategies are better than using a single structure, which is a very limited analysis (reference: 10.1021/acs.jpcb.8b11491). Hence, before docking, it is suitable to perform protein-only MD simulation (in replicates if possible).
It is common that after MD of the complex, analysis of MM/PBSA or MM/GBSA is performed based on the last 50 or 100 frames. When using decomposition analysis, it is possible to see the energetic contribution of each protein residue. This analysis is more robust than simple interaction-type evaluations for ranking important residues. For the version of GROMACS used, the following could be attempted: gmx_mmpbsa tool( https://valdes- tresanco-ms.github.io/gmx_MMPBSA/dev/getting-started/ ).
The figures are very well presented. However, the methodology used here by the authors is very limited since they used only one protein structure to rank the best ligands. The dynamic behavior of proteins should have been considered when working in virtual screening. All works involving molecular dynamics can be performed in replicas. I understand that when working with many structures this could be a hard task, however, it improves the analysis and the results become more reliable. Overall, the description of the results is very limited. The authors analyzed the hydrogen bonds along the trajectory (which was very interesting) and the interaction of clustered complexes. The energetic profile of residue interactions would be more interesting and enriching to rank important residues. The authors should focus on one structure (using replicates and ensemble docking) and publish the results individually.