Energy offset input preparation - following the SSM approach #20
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…tion which matches the new SSM approach, with N undersampling replicas
RiesBen
requested changes
Apr 16, 2021
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| ##make folder | ||
| out_eoff_dir = bash.make_folder(out_eoff_dir) | ||
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| opt_states = "/path/to/a_optimizedState/analysis/data" | ||
| template_imd = "/home/cchampion/tmp/test_area/CHK1_ring/input/template_reeds.imd" |
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This should be a relative path to "../0_input right/template_reeds.imd"?
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Thanks, my idea with writing /path/to/a_optimizedStates/...
was to make sure the user replaces this line with what they want every time they use it.
We could have a default value of opt_states = root_dir + "/a_optimizedState/analysis/data"
for the template imd I just forgot to change, but your suggestion is correct!
I'll make these small changes tomorrow morning and merge it if that is ok with you.
RiesBen
approved these changes
Apr 19, 2021
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@candidechamp
can be merged, after changing the paths as mentioned.
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Hi,
This ended up being a bit more involved than I thought, but what these changes are for are quite simple.
We know from Benjamin's work that we would like to use the SSM approach in all steps of the pipeline. This means that we want to use the coordinates for the optimized states analysis as input for the various pipeline steps.
Prior to these changes, the eoff input would find the undersampling limit from the lower bound analysis, and then go 2 replicas lower (for a total of 3 undersampling replicas). This however means that we would be "missing" some of the conformations from some end states as usually they are more than 3.
Here my approach was to place N (number of end states) replicas between the two bounds found in the lower bound analysis. This means the s_vals.csv file generated now contains that information.
Then I had to modify the eoff submission to take this into account, which meant changing a few things.
The code previously had many options to determine the s-values, I chose to remove the access to these other options. I think it would always be simpler to just write down the values in the csv, and call the code as it is. I didn't remove all of the redundant code, but I could do it as soon as I get your approval.
Note that this code could therefore easily work with an s-distribution that doesn't have N undersampling replicas.