NR Y-complex modelling

For modelling the NR Y-complex the calculation of fit libraries, global optimization and refinement of top “globally optimized” model with Assembline will be used. The dir scnpc_tutorial/NR_Y_complex includes source code files, input files and precalculated modelling results for the NR Y-complex:

- parameters file & configuration file for 'refinement' integrative modelling of wt NR Y-complex
- sequence fasta file, NR_Y_complex_de_novo_model_PDBs, EM (input data)
- NR_Y_complex_final_refined_model.pdb, out (output modelling results)
- NR_Y_complex_de_novo_run (directory with global optimization data):

    - parameters file & configuration file for 'global optimization' integrative modelling of wt NR Y-complex
    - sequence fasta file, input_PDB, EM (input data)
    - NR_Y_complex_de_novo_model.pdb,  out (output modelling results)

    - systematic_fits (directory):

        - parameters file for systematic fitting
        - em_maps, PDB (input data)
        - result_fits_chimera (output fitting results)

  1. First activate your virtual environment and enter the NR_Y_complex/NR_Y_complex_de_novo_run/systematic_fits dir

    source activate Assembline

cd NR_Y_complex/NR_Y_complex_de_novo_run/systematic_fits

    or depending on your computer setup:

    conda activate Assembline

cd NR_Y_complex/NR_Y_complex_de_novo_run/systematic_fits

  2. Run the generation of fit libraries for NR Y-complex rigid bodies with Assembline (results calculated in dir systematic_fits/result_fits_chimera/NR_merged_unerased_tail_relative_clean_v1.3.mrc/)

    fit.py systematic_fitting_parameters.py

  3. The fit libraries have been precalculated and analysed in dir systematic_fits/result_fits_chimera/NR_merged_unerased_tail_relative_clean_v1.3.mrc/. To analyse the fit results on your own run the following

    # while in the systematic_fits/ dir
genpval.py result_fits_chimera

  4. To generate the top five fits of each input rigid body (i.e. top five fits from each fit library) run the following

    #upon successful run of fit.py and genpval
cd result_fits_chimera/search100000_metric_cam_rad_600_inside0.3_res_40

genPDBs_many.py -n5 top5 */*/solutions.csv

#repeat procedure for the other results in result_fits_chimera/ dir

  5. After completing the calculation of fit libraries (or use the precalculated results) enter global optimization project dir (i.e. NR_Y_complex_de_novo_run) and run the global optimization

    cd NR_Y_complex/NR_Y_complex_de_novo_run

# this will run 20000 global optimization modelling runs 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 20000 config.json params.py

    Note

    There is already an output dir NR_Y_complex_de_novo_run/out so in case you want to run the modelling then rename the out/ dir as it will be overwritten from the run above

  6. Enter out/ dir, generate output scoring lists and rebuild atomic structures of models

    cd out

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 NR_Y_complex_de_novo_run> config.json all_scores_sorted_uniq.csv

  7. 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.

  8. 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

    Note

    The four plots from the sampling exhaustiveness analysis are provided only in the CR_Y_complex/out/analysis. Therefore, visit this dir to inspect it and follow the commands above to run on your own.

  9. In order to refine the best globally optimized model of NR Y-complex produced previsouly enter the main project dir (i.e. scnpc_tutorial/NR_Y_complex/) and run the refinement

    # this will run 20000 refinement modelling runs 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 20000 config.json params.py

    Note

    There is already an output dir NR_Y_complex/out so in case you want to run the modelling then rename the out/ dir as it will be overwritten from the run above. Also as you noticed the input PDBs used for refinement were stored in NR_Y_complex_de_novo_model_PDBs for convenience. If you want to run refinement on multiple models in parallel inspect the Assembline manual.

  10. Enter out/ dir, generate output scoring lists and rebuild atomic structures of models

    cd out

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 NR_Y_complex> config.json all_scores_sorted_uniq.csv

  11. As after the global optimization run, 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.

  12. 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