Analyze chemical modifications added to proteins after production, including phosphorylation, acetylation, and ubiquitination. Filter modification sites by confidence, examine surrounding sequence patterns, and compare site quantities while accounting for changes in overall protein abundance.
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Post-translational modification analysis including phosphorylation, acetylation, and ubiquitination. Covers site localization, motif analysis, and quantitative PTM analysis. Use when analyzing phosphoproteomic data or other modification-enriched samples.
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Reference examples tested with: numpy 1.26+, pandas 2.2+, scipy 1.12+
Before using code patterns, verify installed versions match. If versions differ:
pip show <package> then help(module.function) to check signaturespackageVersion('<pkg>') then ?function_name to verify parametersIf code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
"Analyze phosphorylation sites from my proteomics data" → Identify and quantify post-translational modifications including phosphorylation, acetylation, and ubiquitination with site localization and motif analysis.
pyopenms for PTM-aware search, scipy for site-level statisticsPTM_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)
}
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
)
def calculate_ascore_simple(peak_matches_with_ptm, peak_matches_without_ptm, total_peaks):
'''Simplified A-score calculation'''
if peak_matches_without_ptm >= peak_matches_with_ptm:
return 0
p = peak_matches_with_ptm / total_peaks if total_peaks > 0 else 0
if p <= 0 or p >= 1:
return 0
from scipy.stats import binom
p_value = 1 - binom.cdf(peak_matches_with_ptm - 1, total_peaks, 0.5)
return -10 * np.log10(p_value) if p_value > 0 else 100
from collections import Counter
def extract_motifs(sites_df, sequence_col, position_col, window=7):
'''Extract sequence windows around modification sites'''
motifs = []
for _, row in sites_df.iterrows():
seq = row[sequence_col]
pos = row[position_col] - 1 # 0-indexed
start = max(0, pos - window)
end = min(len(seq), pos + window + 1)
# Pad if at sequence boundary
motif = '_' * (window - (pos - start)) + seq[start:end] + '_' * (window - (end - pos - 1))
motifs.append(motif)
return motifs
def count_amino_acids_by_position(motifs, center=7):
'''Count amino acid frequencies by position'''
position_counts = {i: Counter() for i in range(-center, center + 1)}
for motif in motifs:
for i, aa in enumerate(motif):
position_counts[i - center][aa] += 1
return position_counts
library(MSstatsPTM)
# Prepare input from MaxQuant
ptm_input <- MaxQtoMSstatsPTMFormat(
evidence = read.table('evidence.txt', sep = '\t', header = TRUE),
annotation = read.csv('annotation.csv'),
fasta = 'uniprot_human.fasta',
mod_type = 'Phospho'
)
# Process data
processed_ptm <- dataSummarizationPTM(ptm_input, method = 'msstats')
# Differential PTM analysis (adjusting for protein-level changes)
ptm_results <- groupComparisonPTM(processed_ptm, contrast.matrix = comparison_matrix)
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