Author: Jianqing Xu (xubest@gmail.com), Daisuke Kuroda (dkuroda1981@gmail.com), Oana Lungu (olungu@utexas.edu), Jeffrey Gray (jgray@jhu.edu)
Corresponding PI Jeffrey Gray (jgray@jhu.edu).
Last edited 5/17/2020 by Jeliazko Jeliazkov (jeliazkov@jhu.edu) and 5/29/2020 by Rahel Frick (rahel.frick@jhu.edu)
We recommend the following articles for further studies of RosettaAntibody methodology and applications:
B. D. Weitzner*, J. R. Jeliazkov*, S. Lyskov*, N. M. Marze, D. Kuroda, R. Frick, J. Adolf-Bryfogle, N. Biswas, R. L. Dunbrack Jr., and J. J. Gray, "Modeling and docking of antibody structures with Rosetta." Nature Protocols 12, 401–416 (2017)
B. D. Weitzner, D. Kuroda, N. M. Marze, J. Xu & J. J. Gray, "Blind prediction performance of RosettaAntibody 3.0: Grafting, relaxation, kinematic loop modeling, and full CDR optimization." Proteins 82(8), 1611–1623 (2014)
A. Sivasubramanian,* A. Sircar,* S. Chaudhury & J. J. Gray, "Toward high-resolution homology modeling of antibody Fv regions and application to antibody-antigen docking," Proteins 74(2), 497–514 (2009)
Please realize this the overview is to speed you up to run the protocol asap with minimum knowledge. For details of each steps, please check:
Rosetta Antibody can model both antibodies (consisting of the heavy and light chain variable region) and nanobodies (consisting of only the heavy chain variable region). To run the protocol, one needs:
In Rosetta, antibody modeling is a two stage process.
Executable: antibody
First, the sequences are divided into structurally conserved regions (FRH, H1, H2, H3, FRL, L1, L2, and L3) and templates are selected from the database based on BLAST+ score. Alternatively, manual templates can be specified via PDB code and the -antibody:{region}_template, for example: -antibody:l1_template 1rzi. Note that the templates must be present in the database. After selection, template complementarity determining regions are grafted on the template frameworks and the frameworks are assembled according to a template VH–VL orientation (predicting this orientation is challenging, so ten template orientations used). A highly recommended, but optional, FastRelax with constraints is used to alleviate any clashes introduced by grafting.
Sample command line: antibody.macosclangrelease -fasta antibody_chains.fasta | tee grafting.log.
Here antibody_chains.fasta looks like:
>heavy
VKLEESGGGLVQPGGSMKLSCATSGFRFADYWMDWVRQSPEKGLEWVAEIRNKANNHATYYAESVKGRFTISRDDSKRRVYLQMNTLRAEDTGIYYCTLIAYHYPWFAYWGQGTLVTVS
>light
DVVMTQTPLSLPVSLGNQASISCRSSQSLVHSNGNTYLHWYLQKPGQSPKLLIYKVSNRFSGVPDRFSGSGSGTDFTLKISRVEAEDLGVYFCSQSTHVPFTFGSGTKLEIKRHeavy-chain only antibodies can also be modelled by only specifying a heavy chain and the flag -vhh_only.
The typical runtime, with FastRelax, is ~20 mins per model or ~3 hours for 10 models.
As of April 27th, 2019, there is a new database in additional_protocol_data, which has some slight formatting changes from the old one. Switching over to the new database is easy and can be done by running git submodule update --init --recursive for the git-tracked version of Rosetta. Presumably, the additional data repository should be downloadable for general users as well.
Executable: antibody_H3
Next, for each grafted model, the CDR H3 is de novo modeled and the relative VH–VL orientation is refined via local docking. Flags are split into a simulation set and a loop-modeling set. Both sets of flags are shown below (the loop modeling flags can also be found in tools/antibody/abH3.flags). If loop modeling is slow, it can be expedited by decreasing KIC sampling via the flags -loops:refine_outer_cycles 2 and -loops:max_inner_cycles 20, however these flags have not been benchmarked. If using multiple VH–VL orientations, we recommend 1000 structures be generated for the top grafted model (typically, model-0.relaxed.pdb) and 200 structures be generated for the other orientations.
Sample command line (as of May 17th, 2020): antibody_H3.macosclangrelease @flags.
flags:
# input grafted model
-s grafting/model-0.relaxed.pdb
# recommended number of structs
-nstruct 1000
# constraints are enabled by default, so flags are shown just to indicate that they can be turned off
# recommended as kink is present in 90% of Abs and as VH-VL Q-Q is present in 808%
-antibody:h3_loop_csts_lr true
-antibody:h3_loop_csts_hr true
-antibody:auto_generate_h3_kink_constraint true
-antibody:constrain_vlvh_qq true
-constraints:cst_weight 1.0
# standard settings, for packages used by antibody_H3
-ex1
-ex2
-extrachi_cutoff 0
# necessary if running multiple procs w/o MPI
-multiple_processes_writing_to_one_directory
# specify output file
-out:file:scorefile H3_modeling_scores.fasc
# specify output folder
-out:path:pdb H3_modeling The typical runtime varies, based on CDR H3 length (due to KIC). In our benchmark, the runtime was ~1 hour per model. We highly recommend using a cluster to speed up calculations.
Additionally, models should be validated for a reasonable VH–VL orientation. This can be done with the following command: python $ROSETTA/main/source/scripts/python/public/plot_VL_VH_orientational_coordinates/plot_LHOC.py.
Flags prior to May 17th 2020
Sample command line (as of June 24th, 2019): antibody_H3.macosclangrelease @flags.
flags:
# input grafted model
-s grafting/model-0.relaxed.pdb
# recommended number of structs
-nstruct 1000
# enable contraints (probably should just detect weight in future)
-antibody:constrain_cter
-constraints:cst_weight 1.0
# recommended kink cst, kink present in 90% of Abs
-antibody:auto_generate_kink_constraint
-antibody:all_atom_mode_kink_constraint
# recommended VH-VL Q-Q constraint, you must manually specify the file (see integration test)
-antibody:constrain_vlvh_qq
# standard settings, for packages used by antibody_H3
-ex1
-ex2
-extrachi_cutoff 0
# necessary if running multiple procs w/o MPI
-multiple_processes_writing_to_one_directory
# specify output file
-out:file:scorefile H3_modeling_scores.fasc
# specify output folder
-out:path:pdb H3_modeling You can use a set of decoys simultaneously for antibody-antigen docking simulations, such as SnugDock and EnsembleDock.