Nup116 k.o. ScNPC (at 37C) modellingΒΆ

For modelling the Nup116 k.o. ScNPC (at 37C) the local rigid body refinement method from Assembline will be used. The dir scnpc_tutorial/Nup116delta37C includes source code files, input files and precalculated modelling results for the Nup116 k.o. ScNPC model (37C):

- parameters files & configuration files for 'refinement' integrative modelling of Nup116delta37C ScNPC
- sequence fasta file, input_PDB, EM (input data)
- ScNPC_Nup116delta37C_final_model.pdb, ScNPC_Nup116delta37C_IR_final_model.pdb, ScNPC_Nup116delta37C_NR_final_model.pdb, out_IR, out_NR (output modeling results)

  1. First activate your virtual environment and enter the Nup116delta37C dir

    source activate Assembline

cd Nup116delta37C/

    or depending on your computer setup:

    conda activate Assembline

cd Nup116delta37C/

  2. Run the refinement for NR Y-complex and half-assembly of IR unit (two separate refinement runs)

    # this will run 500 refinement modelling runs for NR Y-complex on a slurm cluster (for options/parameters or local runs inspect the Assembline manual)
assembline.py --traj --models -o out --multi --start_idx 0 --njobs 500 config_NR.json params_NR.py

# this will run 500 refinement modelling runs for IR unit subunits on a slurm cluster (for options/parameters or local runs inspect the Assembline manual)
assembline.py --traj --models -o out --multi --start_idx 0 --njobs 500 config_IR.json params_IR.py

    Note

    There are already output dirs Nup116delta37C/out_NR and Nup116delta37C/out_IR so in case you want to run the modelling then rename the out_IR/ and/or out_NR/ dir as it will be overwritten from the run above

  3. Enter any of the out/ dir (e.g. out_NR/), generate output scoring lists and rebuild atomic structures of models

    #these steps can be followed for any of the out_NR and out_IR
cd out_NR

extract_scores.py #this should create a couple of files including all_scores_sorted_uniq.csv

rebuild_atomic.py --top 10 --project_dir <full path to the original project directory Nup116delta37C> config_NR.json all_scores_sorted_uniq.csv

  4. While in the out/ dir run the following command to prepare your output models for analysis with imp-sampcon tool from IMP

    setup_analysis.py -s all_scores.csv -o analysis -d density.txt

    Note

    The density.txt is not provided but only in the CR_Y_complex/out, therefore visit this dir to inspect it. To generate it yourself please inspect Assembline analysis section.

  5. Run imp-sampcon exhaust tool (command-line tool provided with IMP) to perform the sampling analysis:

    cd analysis

imp_sampcon exhaust -n <prefix for output files> \
--rmfA sample_A/sample_A_models.rmf3 \
--rmfB sample_B/sample_B_models.rmf3 \
--scoreA scoresA.txt --scoreB scoresB.txt \
-d ../density.txt \
-m cpu_omp \
-c <int for cores to process> \
-gp \
-g <float with clustering threshold step> \

    Note

    For further descriptions of settings for imp_sampcon please see Sampling exhaustiveness and precision with Assembline