Protein Quantification

Label-Free Quantification (LFQ)

Intensity-Based (MaxLFQ Algorithm)

import pandas as pd import numpy as np

def maxlfq_normalize(intensities): '''Simplified MaxLFQ normalization''' log_int = np.log2(intensities.replace(0, np.nan))

# Median centering per sample
sample_medians = log_int.median(axis=0)
global_median = sample_medians.median()
normalized = log_int - sample_medians + global_median

return normalized

Spectral Counting

def spectral_count_normalize(counts, total_spectra): '''Normalized spectral abundance factor (NSAF)''' # Divide by protein length, then by total nsaf = counts / total_spectra return nsaf / nsaf.sum()

TMT/iTRAQ Quantification

library(MSnbase)

Load reporter ion data

tmt_data <- readMSnSet('tmt_data.txt')

Normalize with reference channel

tmt_normalized <- normalize(tmt_data, method = 'center.median')

Summarize to protein level

protein_data <- combineFeatures(tmt_normalized, groupBy = fData(tmt_data)$protein, fun = 'median')

Python TMT Processing

def extract_tmt_intensities(spectrum, reporter_mz, tolerance=0.003): '''Extract TMT reporter ion intensities''' mz, intensity = spectrum.get_peaks() tmt_intensities = {}

for channel, target_mz in reporter_mz.items():
    mask = np.abs(mz - target_mz) < tolerance
    if mask.any():
        tmt_intensities[channel] = intensity[mask].max()
    else:
        tmt_intensities[channel] = 0

return tmt_intensities

TMT_10PLEX = {'126': 126.127726, '127N': 127.124761, '127C': 127.131081, '128N': 128.128116, '128C': 128.134436, '129N': 129.131471, '129C': 129.137790, '130N': 130.134825, '130C': 130.141145, '131': 131.138180}

SILAC Quantification

def calculate_silac_ratio(heavy_intensity, light_intensity): '''Calculate SILAC H/L ratio''' if light_intensity > 0 and heavy_intensity > 0: return np.log2(heavy_intensity / light_intensity) return np.nan

Typical mass shifts

SILAC_SHIFTS = { 'Arg10': 10.008269, # 13C6 15N4 Arginine 'Lys8': 8.014199, # 13C6 15N2 Lysine 'Arg6': 6.020129, # 13C6 Arginine 'Lys6': 6.020129 # 13C6 Lysine }

MSstats Workflow (R)

library(MSstats)

Prepare input from MaxQuant

maxquant_input <- MaxQtoMSstatsFormat( evidence = read.table('evidence.txt', sep = '\t', header = TRUE), proteinGroups = read.table('proteinGroups.txt', sep = '\t', header = TRUE), annotation = read.csv('annotation.csv') )

Process and normalize

processed <- dataProcess(maxquant_input, normalization = 'equalizeMedians', summaryMethod = 'TMP', censoredInt = 'NA')

Protein-level summary

protein_summary <- quantification(processed)

Related Skills

• data-import - Load MS data before quantification

• differential-abundance - Statistical testing after quantification

• expression-matrix/counts-ingest - Similar matrix handling