dotsdl
commented
2 years ago Raw notes from story review, shared here for visibility:
- our input network approach must be able to accommodate protein mutations for RBFE, not just ligand transformations
- should allow for network with some edges encoding a protein mutation, others encoding ligand transformations, etc.
In broad terms, what are you trying to do?
For the COVID Moonshot second-generation inhibitor program (and its successor ASAP), we will need to assess the potential for point mutations in the target protein to significantly reduce the affinity for small molecule ligands of interest. This will involve executing many relative free energy calculations in which the protein is mutated, but the ligand is identical. Transformation networks may additionally take advantage of both transformations between related ligands and between point mutants of the same target protein.
How do you believe using this project would help you to do this?
The same infrastructure can be used to set up, execute, analyze, and serve up data for both point mutations of a target protein and transformations among related ligands. We just need to be careful that the data structures can be extended to support this.
What problems do you anticipate with using this project to achieve the above?
Nearly all the same components for setting up and executing ligand modification transformations can be re-used here, but the data models will need to support cases where the protein target is being mutated, rather than the ligand being modified.