Version Compatibility

Reference examples tested with: numpy 1.26+, pandas 2.2+, scipy 1.12+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Post-Translational Modification Analysis

"Analyze phosphorylation sites from my proteomics data" → Identify and quantify post-translational modifications including phosphorylation, acetylation, and ubiquitination with site localization and motif analysis.

• Python: pyopenms for PTM-aware search, scipy for site-level statistics
• CLI: MaxQuant with variable modifications for enrichment-based PTM analysis

Common PTMs and Mass Shifts

PTM_MASSES = {
    'Phosphorylation': 79.966331,      # STY
    'Oxidation': 15.994915,             # M
    'Acetylation': 42.010565,           # K, N-term
    'Methylation': 14.015650,           # KR
    'Dimethylation': 28.031300,         # KR
    'Trimethylation': 42.046950,        # K
    'Ubiquitination': 114.042927,       # K (GlyGly remnant)
    'Deamidation': 0.984016,            # NQ
    'Carbamidomethyl': 57.021464,       # C (fixed mod from IAA)
}

Processing MaxQuant PTM Output

Goal: Extract high-confidence phosphorylation sites from MaxQuant output with proper filtering and site annotation.

Approach: Load the Phospho(STY)Sites table, remove reverse hits and contaminants, filter by localization probability, and construct gene-level site identifiers.

import pandas as pd
import numpy as np

# Phospho(STY)Sites.txt from MaxQuant
phospho = pd.read_csv('Phospho (STY)Sites.txt', sep='\t', low_memory=False)

# Filter valid sites
phospho = phospho[
    (phospho['Reverse'] != '+') &
    (phospho['Potential contaminant'] != '+')
]

# Filter by localization probability
phospho_confident = phospho[phospho['Localization prob'] >= 0.75]
print(f'Confident sites (prob >= 0.75): {len(phospho_confident)}')

# Extract site information
phospho_confident['site'] = phospho_confident.apply(
    lambda r: f"{r['Gene names']}_{r['Amino acid']}{r['Position']}", axis=1
)