bsc-cns-eapm / brewpitopes

Set of tools to manage epitope prediction results from linear and structural origin and to integrate a pipeline of prioritization filters.

BREWPITOPES

Set of tools to manage epitope prediction results from linear and structural origin and to integrate a pipeline of prioritization filters to curate B-cell epitopes specific for neutralizing antibody recognition.

PIPELINE

1. Create an R project for the corresponding protein.
   Save the Rdata for the corresponding protein.
2. Use directories.R to create the folder environment.
3. Download the FASTA file of the target protein at Uniprot.
   Save at /Z_fasta
4. Use the FASTA to predict linear epitopes using Bepipred and export results as csv (default parameters).
   https://services.healthtech.dtu.dk/service.php?BepiPred-2.0
   Save at path/to/A_linear_predictions/bebipred
5. Extract epitopes from Bepipred results using epixtractor_linear_bebipred.py.
   python3 epixtractor_linear_bebipred.py
   path/to/bepipred_results.csv
   Copy output to /C_epixtractor.
6. Use the FASTA to predict linear epitopes using ABCpred.
   https://webs.iiitd.edu.in/raghava/abcpred/ABC_submission.html
   Predict using all the epitope windows and overlapping filter ON.
   Copy results and save as .csv
   Save at path/to/A_linear_predictions/abcpred
7. Extract epitopes from ABCpred results using epixtractor_linear_abcpred.R
   Add paths to ABCpred result files.
   Follow the instructions in the R file.
   Copy output to /C_epixtractor
8. Download the PDB file of the target protein at PDB DB. Save at /B_structural_predictions/pdb
9. Use PDBrenum to renumerate the PDB residues according to its corresponding FASTA file in Uniprot.
   http://dunbrack3.fccc.edu/PDBrenum/
   Download results as .pdb
   Save at /B_structural_predictions/pdbrenum
10. Use the renumbered PDB to predict structural epitopes using Discotope and export the results as csv.
    https://services.healthtech.dtu.dk/service.php?DiscoTope-2.0
    Default threshold.
    Select chain A by default. Save at /B_structural_predictions/discotope
11. Extract epitopes from Discotope results using epixtract_structural.py
    python3 epixtract_structural.py
    Save at path/to/B_structural_predictions/discotopediscotope_results.csv
12. Use epimerger.py to merge the epitopes extracted from Bepipred, ABCpred and Discotope results.
    Add the paths to the corresponding files at C_epixtractor folder.
    Follow the instructions in the R file.
    Save at /D_epimerger
13. Predict the protein topology using CCTOP.
    http://cctop.enzim.ttk.mta.hu/?_=/jobs/submit
    Donwload results as .xml. Save at /E_topology/CCTOP
14. Extract the extraviral domains using xml_cctop_parser.R
    Follow the instructions at the R file.
15. Use epitopology.R to label the epitopes based on the extraviral domains.
    Follow the instructions at the R file.
    Save results at /E_topology
16. Predict the glycosilation profile of the protein using the FASTA file.
    N-GLYCOSILATIONS AT:
    https://services.healthtech.dtu.dk/service.php?NetNGlyc-1.0
    SAVE THE DATAFRAME HEADED: SeqName Position Potential Jury_agreement NGlyc_result Prediction
    AS CSV
    O-GLYCOSILATIONS AT:
    https://services.healthtech.dtu.dk/service.php?NetNGlyc-1.0
    SAVE THE DATAFRAME HEADED: seqname source feature start end score strand frame comment
    AS CSV
17. Use epiglycan_extractor.R to extract the glycosilated positions from both N-glyc and O-glyc outputs.
    Follow the instructions at the R file.
    Save at /F_epiglycan
18. Use epiglycan.py to label the glycosilated epitopes.
    python3 epiglycan.py
    Upload the file obtained at step 15: /E_epitopology/XXX_epitopology.csv
    Upload the glycosilated positions: /F_epiglycan/glycosilated_positions.csv
    Save as /F_epiglycan/XXX_epitopology_glycans.csv
19. Use ICM_browser (MOLSOFT) to extract the RSA values for accessibility calculation.
    Download ICM_browser from http://www.molsoft.com/icm_browser.html
    Open the PDB renumbered file of the corresponding protein (step 9)
    Execute in the command line of the programme the code in Compute_ASA.icm
    Save results at /G_episurf
20. Use icm_extractor.R to extract the buried positions.
    Follow the instructions at the R file.
    Save at /G_episurf
21. Use episurf.py to label the epitopes with buried residues.
    python3 episurf.py
    Upload the file generated at step 18: path/to/F_epiglycan/XXX_epitoplogy_glycans.csv
    Upload the buried positions at step 20: path/to/G_episurft/buried_positions.csv
    Save as /G_episurf/XXX_epitoplogy_glycans_surf.csv
22. Use epifilter.R to retain the epitopes that are extraviral, non-glycosilated, exposed and length >= 5.
    Follow the R file instructions.
    Save at /I_final_candidates.
23. Use epicontig.ipynb (Jupiter Notebook) to extract the epitopic regions / contigs.
    Upload the candidates_df.csv generated at step 22. Follow the instructions in the Notebook.
24. Use yield_plot.R to plot the results of the pipeline.
    Follow the instructions in the R file.

APENDIX FOR VARIANTS OF CONCERN

1. Generate FASTA using fasta_mutator.R
   Download reference FASTA from Spike protein from UniprotKB.
   Upload where indicated at script instructions.
   Upload the mutations of the corresponding VOC found as attached files in this Github. (ie Gamma = 20211203_spike_gamma_vocs.csv) Execute the script and save the VOC Fasta file.
   Once saved, remove "" from the file to obtain a properly formated FASTA.
   Start the pipeline above with the mutated FASTA file.