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Thanks!
With the multi-refined models, how can I make an average map for all
the models?
Can I do this in phenix?
Fengyun
引用 Pavel Afonine
Hi Fengyun,
- to run many SA with different random seeds:
for random_seed in random_seeds: phenix.refine model.pdb data.mtz simulated_annealing=true main.random_seed=random_seed output.prefix=random_seed
To create even broader variety, I would slightly shake the starting model:
for random_seed in random_seeds: phenix.refine model.pdb data.mtz simulated_annealing=true main.random_seed=random_seed output.prefix=random_seed modify_start_model.sites.shake=0.3
Note, you will need to filter the outliers - refined models that have significantly higher R-factors compared to the rest.
I read on the paper that they use CNS (model_map.inp) to calculate the avarage sigmaA-weighted 2fofc map. Right now I have set up the simulated annealing with different random seeds. I wonder whether phenix could calculate the average map with the multiple coordinates from different starts?