Documentation created by Brahm Yachnin (email@example.com), Khare laboratory and Chris Bailey-Kellogg (firstname.lastname@example.org). Parts of this documentation are copied/adapted from Vikram K. Mulligan's (email@example.com) design-centric guidance documentation. Last edited May 8, 2019.
A detailed explanation of packer-compatible de-immunization in Rosetta is described in the MHCEpitopeEnergy documentation. If your goal is to apply de-immunization globally to an entire protein/pose, it is simple enough to configure the de-immunization protocol at the scorefunction level and use that scorefunction for all relevant design movers.
An alternative approach, which attempts to preserve the protein sequence by targeting only the "hottest" immunogenic regions, can be implemented using the
AddMHCEpitopeConstraintMover. This also makes using more sophisticated epitope prediction methods, like the NetMHCII predictor, that require a pre-computed external database to be feasible. We have provided the mhc-energy-tools to help you identify hotspots in your protein, and generate an external database if desired.
Generally speaking, the
AddMHCEpitopeConstraintMover will de-immunize a pose only over the residues specified by the
selector. The configuration of the epitope predictor can be the same as the scorefunction configuration, or it can be different by passing a unique
.mhc file. (Note that you can even turn on the scoreterm without any configuration, and apply a configuration using constraints only. In this case, de-immunization will only be performed at the constraint-specified positions and configuration.)
You can also use
AddMHCEpitopeConstraintMover to add a second epitope prediction method to the entire pose by applying it without a selector.
Note that for the constraint to be function, YOU MUST USE A SCOREFUNCTION WITH
mhc_epitope WEIGHTED TO SOMETHING OTHER THAN 0. The
weight parameter passed to the constraint mover will be multiplied by scorefunction weight to give you the "net weight." If you scorefunction has
mhc_epitope weighted to 0, it will therefore have a net weight of 0.
Please see the main MHCEpitopeEnergy page for citation information if you use the
Autogenerated Tag Syntax Documentation:
Add mhc epitope constraints from the provided file to the selected region or whole pose.
<AddMHCEpitopeConstraintMover name="(&string;)" selector="(&string;)" filename="(&string;)" weight="(1.0 ℜ)" />
The ClearCompositionConstraintsMover mover will remove all MHCEpitopeConstraints, along with any other composition constraints. Of course, this will not remove predictors configured at the scorefunction level, only those configured at the constraint level.