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Nsp3 Mac1 Fragment Screening

This folder contains instructions and scripts for processing an Nsp3 Mac1 fragment screening dataset. To complete the analysis, rs-booster(https://github.com/rs-station/rs-booster) must be installed. Here, we

  1. run careless with a multivariate prior, and for many values of the double-Wilson r parameter on 16 holo drug fragment-screening datasets.
  2. inspect CCpred and difference map peak heights.

We start with MTZ files found in ./20221007_unscaled_unmerged. These were converted from unmerged .hkl files provided by Galen Correy, for (Schuller et al. Sci. Adv. 2021, DOI: 10.1126/sciadv.abf8711).

To run careless, we use the script careless_runs/slurm-dw-array-grid.sh, which starts a slurm batch array job. This job requires careless_runs/slurm_params.txt, in which we vary the double-Wilson r value across the individual careless runs. To call using slurm:

cd careless_runs
sbatch slurm-dw-array-grid.sh

Many bash scripts require activating a conda environment with careless in it. Please take note that you are activating the right conda environment!

Then, we evaluate the quality of the careless results in the jupyter notebook Inspect_Careless_param_grid.ipynb. Inspect_Careless_param_grid.ipynb calls the run_ccs.sh script that uses careless to compute correlation coefficients of careless results, as well as the make_diffmap.sh script that uses reciprocalspaceship to compute difference maps and peak heights. This notebooks also plots figures. To minimize bias, we refine phases against the reference dataset merged with a univariate prior.

Folders and files

  • 20221007_unscaled_unmerged: a folder with sixty-five holo drug fragment screening datasets and one reference drug fragment screening dataset. Each subfolder contains a model as well as several .mtz files that have been preprocessed using scripts/hkl2mtz.sh, as well as clean_datasets_compute_diffmaps.ipynb, a Jupyter notebook that corrects for indexing ambiguities and adds metadata columns to the .mtz files in preparation for careless.

  • careless_runs: a folder containing a script for running careless as a batch array, as well as the resultant subfolders containing outputs from individual runs of careless.

  • pymol_dfs: a folder with pymol inputs and outputs.

  • scripts: a folder with scripts and two mtzs.

    • DFS_refine_mv.mtz: an MTZ file with phases refined from 7kqo.pdb and a dataset merged with a multivariate prior (careless_runs/merge_20379915_20830_mono_mc1_10k_grid_6/dfs_1.mtz).
    • DFS_refine_uv.mtz: an MTZ file with phases refined from 7kqo.pdb and a dataset merged with a multivariate prior (careless_runs/merge_20379918_5300_mono_mc1_10k_grid_2/dfs_1.mtz).
    • hkl2mtz.sh: a script that uses careless to convert .hkl files to .mtz files.
    • make_diffmap_all.sh: A script that uses rs-booster to compute difference maps given a folder with careless runs in it.
    • make_diffmap_indiv.sh: A script that uses rs-booster to computes difference maps of a single careless run.
    • run_ccs_all.sh: A script that computes CCpred given a folder with careless runs in it.

  • Inspect_Careless_param_grid-DFS.ipynb: a notebook for evaluating the quality of the careless results.

  • compute_pandda_peak_height.ipynb: a notebook for peak height calculation of the Z-maps.