Protein Affinity Maturation Mutation Design
In immunology, affinity maturation is the process by which TFH cell-activated B cells produce antibodies with increased affinity for antigen during the course of an immune response. With repeated exposures to the same antigen, a host will produce antibodies of successively greater affinities. A secondary response can elicit antibodies with several fold greater affinity than in a primary response. Affinity maturation primarily occurs on surface immunoglobulin of germinal center B cells and as a direct result of somatic hypermutation (SHM) and selection by TFH cells.
Calculation principle
Rosetta Flex ddG is the latest protein affinity maturation module released in 2018. It considers the backbone flexibility of the protein complex interface, uses a multi-conformation set to evaluate the average mutation binding free energy change, and uses the GAM model to correct the energy score to make it more in line with the experimental results. A large number of test results show that the Rosetta Flex ddG module has better prediction results than ddG monomer in predicting the small-to-large mutation group, multiple mutations, none to alanine group, single mutation to alanine group, and antibodies group.
Therefore, Flex Ddg is suitable for the following scenarios:
- It is suitable for alanine scanning on the interface of complexes to determine hot spot residues.
- Predict the change of binding free energy for the mutation of large-volume amino acids into small-volume amino acids.
- Multi-point joint mutation & antibody affinity maturation.
The calculation process is as follows
- First, use the initial structure information to generate constraints, and minimize energy under this condition;
- Use Backrup Mover to sample the skeletal structure of the wild type and mutant groups respectively;
- Mutate, repack and minimize energy according to the new bone structure;
- Score the new conformation, average the scores of multiple conformations, and GAM fit.
Tutorials
Configure run.py
First, set up run.py for the system environment
# Modify the actual installation path of Rosetta in line 13
rosetta_scripts_path = os.path.expanduser("~/rosetta/source/bin/rosetta_scripts")
# After modification, adjust the example according to your own installation path.
rosetta_scripts_path = os.path.expanduser("/usr/local/rosetta_src_2019.21.60746_bundle/main/source/bin/rosetta_scripts.mpi.macosclangrelease")
Prepare the input file
The prepared PDB input file, Resfile, chains_to_move.txt, etc. must be placed in a folder named inputs.
Set operating parameters:
Open run.py and modify some of the following operating parameters.
nstruct = 3
nstruct = 3
max_minimization_iter = 5
abs_score_convergence_thresh = 200.0
number_backrub_trials = 10
backrub_trajectory_stride = 5
path_to_script = 'ddG-backrub.xml'
Run Flex Ddg
python run.py
After the calculation is completed, the result will be automatically stored in the output folder.
Result analysis
python analyze_flex_ddG.py output
All results will be saved in the output-struct_scores_results.csv file.
* For Research Use Only.
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