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Nup82 Complex

Modeling of the Nup82 subcomplex of the Nuclear Pore Complex PubMed logo PDB-Dev

tickVerified to work with the latest stable IMP release (2.9.0). The files are also available at GitHub.
Additional software needed to use these files: IMP MODELLER scikit-learn matplotlib biopython install instructions

Anaconda logo To install the software needed to reproduce this system with the Anaconda Python command line tool (conda), run the following commands:

conda config --add channels salilab
conda install imp modeller scikit-learn matplotlib biopython

UCSF logo To set up the environment on the UCSF QB3 cluster to run this system, run:

module load imp modeller python/scikit python/matplotlib
Tags chemical crosslinks EM class average MODELLER PMI

DOI

These scripts demonstrate the use of IMP, MODELLER, and PMI in the modeling of the Nup82 complex using DSS/EDC chemical cross-links and electron microscopy (EM) 2D class averages.

First, MODELLER is used to generate initial structures for the individual components in the Nup82 complex. Then, IMP is used to model these components using DSS/EDC crosslinks and the electron microscopy 2D class averages for the entire Nup82 complex.

The modeling protocol will work with a default build of IMP, but for most effective sampling, IMP should be built with MPI so that replica exchange can be used.

List of files and descriptions:

cc_tr1: Nsp1_637_727 + Nup82_522_612 + Nup159_1211_1321 (using 5CWS as templates)

cc_tr2: Nsp1_742_778 + Nup82_625_669 + Nup159_1332_1372 (using 5CWS as templates)

cc_tr3: Nsp1_788_823 + Nup82_678_713 + Nup159_1382_1412 (using 5CWS as templates)

1_run_initial_random_EMclass2.sh: script that runs the PMI script 1_modeling_initial_random.py.

2_run_refinement.sh: script that runs the PMI script 2_modeling_allEM_except11_19.py.

(1) Running the IMP/PMI scripts for the initial stage of modeling

  1. python 1_modeling_initial_random.py -r repeats -out outputdir -em2d class_average -weight em2d_weight

The script will generate independent and uncorrelated trajectories from different random initial configuration restrained by all data and information available for the system (e.g., chemical cross-linking data, excluded volume, structures) and restrained by one EM 2D class average.

(2) Running the IMP scripts for the filtering stage of modeling:

First, we use IMP to register models against EM class averages and then select models that satisfy those class averages the best. Here, the filtering is applied to the Nup82 Complex. The filtering protocol will work with a default build of IMP. The scripts require GNUplot and ImageMagick.

  1. python EM2D-Filter.py input_rmf_file list_of_class_averages angstrom_per_pixel number_of_projections model_resolution image_resolution frame_of_rmf_to_read

Inputs: input_rmf_file: structure to be projected and registered against class averages list_of_class_averages: text file listing the list of class averages. Output: A list of score for the structure against each image (1-cross correlation).

  1. bash Get-Distribution-Satistics.sh Inputs: List of files containing the scores. This is hard coded in the script here. Outputs: Histogram of the score for each images given a set of models Automatically made plots for the distributions of the scores Satistics (average, standard deviation, min, max) for the score of a set of models given a class average.

(3) Running the IMP/PMI scripts for the refinement stage of modeling

  1. python 2_modeling_allEM_except11_19.py -r repeats -out outputdir -rmf starting_rmf -rmf_n rmf_frame -em2d class_average -weight em2d_weight

The script will generate independent and uncorrelated trajectories, refined from the best-scoring configurations after the EM filter. All data and information available for the system, including chemical cross-linking data, excluded volume, atomic structures, and 21 good EM 2D class averages (except classes 11 and 19) are used.

Information

Author(s): Seung Joong Kim, Ilan E. Chemmama, Riccardo Pellarin

Date: October 6th, 2016

License: CC BY-SA 4.0 This work is licensed under the Creative Commons Attribution-ShareAlike 4.0 International License.

Last known good IMP version: build info build info

Testable: Yes.

Parallelizeable: Yes

Publications: