FPICS-Iodination-Quant

Quantify site specific iodination using PSM level data

This Jupyter notebook quantifies iodination events on histidine (H) and tyrosine (Y) residues in peptide-spectrum match (PSM) data.

Features

• Loads PSM data from a TSV file
• Filters and processes peptides with modifications
• Identifies and quantifies iodination on H/Y residues
• Calculates frequency and percentage of iodination per residue

Requirements

• Python 3.x
• pandas

Usage

1. Prepare your PSM data

   • Ensure you have a psm.tsv file with columns: Peptide, Modified Peptide, Protein Start, Protein End, Assigned Modifications, Entry Name.

2. Set the file path

   • Edit the filepath variable in the notebook to point to your psm.tsv file:

     filepath = "/path/to/your/psm.tsv"

3. Run all cells

   • Execute each cell in order. The notebook will:
     • Load and clean the data
     • Extract and process modifications
     • Quantify iodination events
     • Output frequency and percentage tables

4. Customize protein entry

   • To analyze a specific protein, change the entry_name argument in:

     iodo_quant(merge_psm, "MYG_HORSE")

     Replace "MYG_HORSE" with your protein of interest.

Output

• Tables showing iodination frequency and percentage for H/Y residues in the selected protein.

Example

# Set file path
filepath = "/Users/nithesh/Documents/Iodo_script/20240227_FPI_apomyoglobin_NvD_plus-minusHis-correctDB/2024_02_25_KB_NoFAIMS_MV_004_A2/psm.tsv"

# Run quantification
iodo_quant_results = iodo_quant(merge_psm, "MYG_HORSE")
total_pep_appearances(iodo_quant_results, merge_psm, psm_clean)