Skip to content

Latest commit

 

History

History
31 lines (25 loc) · 1.63 KB

README.md

File metadata and controls

31 lines (25 loc) · 1.63 KB

Overview

Accompanying code repository for the publication Multivalent Binding Resolved by Fluorescence Proximity Sensing by Schulte et al. (2022). Manuscript submitted for publication.

Setup

  1. Clone the PEPTIDE REACToR repo and follow the installation instructions:
    git clone [email protected]:spaenigs/peptidereactor.git.
  2. Create directory data/fps/ and insert data (see supplementary data of manuscript).
  3. Copy nodes/fps into the node directory.
  4. Replace the original main.py with ./main.py (recommended) or add the following rule after aggregate_directories: 
    w.add(fps.append_linker.rule(
     dir_in=f"data/temp/{TOKEN}/{{dataset}}/all/",
     linker_in="data/{dataset}/data.yaml",
     dir_out=all_encodings_dir, benchmark_dir=w.benchmark_dir))

Manual execution

  1. Store peptide names in data/fps/all_names.txt and sequences in data/fps/all_peptides.txt.
  2. Execute scripts/process_linkers.py to create the required seqs.fasta and classes.txt files.
  3. Execute main.py. This will generate all possible encoded datasets, and append the linkers to the encoded sequences.
  4. Execute scripts/parse_and_add_kd.py to replace the dummy classes with the actual KD values.
  5. Execute scripts/regression_final.py to run the analysis. Replace "data/fps/csv/all_kd/dist_freq_dn_100_dc_100.csv" with the path to your desired encoded dataset.

Automated analysis and graphics

  1. Run worklflows/process_data.smk (requires Snakemake, see: https://snakemake.readthedocs.io/en/stable/).
  2. Run workflows/group_assoc.smk to visualize the different iterations.