bio-proteomics-ptm-analysis

name	bio-proteomics-ptm-analysis
description	Frames PTM/phosphoproteomics analysis as three stacked inference layers on a biased enrichment - chemistry selection, site localization (FLR), and protein-level-adjusted quantification with MSstatsPTM - plus kinase-activity and functional triage. Covers MaxQuant Phospho (STY)Sites multiplicity expansion, localization-probability filtering (class I, Ascore, ptmRS, DIA EG.PTMLocalizationProbabilities, DIA-NN PTM.Site.Confidence), false localization rate (LuciPHOr/DeepFLR), motif analysis with experiment-matched backgrounds, diGly/K-GG ubiquitin specificity, acetyl/glyco traps, and KSEA/PTM-SEA. Use when localizing and quantifying phosphorylation, acetylation, ubiquitination, or glycosylation sites from enrichment-based runs and deciding whether an apparent site change is real after subtracting protein abundance. Peptide ID and open/variable-mod search is peptide-identification; underlying protein-level quant is quantification and differential-abundance; DIA acquisition mechanics is dia-analysis.
tool_type	mixed
primary_tool	MSstatsPTM

Version Compatibility

Reference examples tested with: MSstatsPTM 2.4+, pandas 2.2+, numpy 1.26+, 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.

PTM and Phosphoproteomics Analysis -- Three Inference Layers Stacked on a Biased Extraction

"Find the regulated phosphosites in my enriched samples" -> Localize each modification, then test whether its abundance change survives subtracting the protein-level change -- because a PTM result is three separate inferences (enrichment, localization, quantification) and each fails silently if the layer below is treated as solved.

• R: MSstatsPTM::groupComparisonPTM() for protein-adjusted site testing (the load-bearing tool)
• Python: pandas to expand MaxQuant Phospho (STY)Sites multiplicity and filter localization probability
• R: KSEAapp / PTM-SEA (ssGSEA2.0) for kinase-activity inference from the site fold-changes

Scope: this skill OWNS enrichment-chemistry framing, site localization and FLR, multiplicity-resolved site quant, protein-level adjustment, motif analysis, and kinase-activity inference. Peptide identification and open/variable-mod search route to peptide-identification; the underlying protein-level (unenriched) quant routes to quantification and differential-abundance; DIA acquisition mechanics route to dia-analysis. OUT OF SCOPE: intact-glycopeptide glycan-composition search (pGlyco3/MSFragger-Glyco) and absolute occupancy from three-ratio SILAC are noted but not implemented here.

The Single Most Important Modern Insight -- A PTM Result Is Three Inferences, Not One

1. Enrichment IS the experiment, and the chemistry is a filter confounded with biology. The data only contain what the chemistry captured. TiO2 and Fe-IMAC give partially-overlapping phosphoproteomes; anti-K-GG enriches ubiquitin, NEDD8, and ISG15 indistinguishably; a lectin reports only its cognate glycoforms. A between-method or between-lab "biological difference" must FIRST be excluded as a chemistry artifact before it is called biology.
2. Identifying a peptide is NOT localizing the modification. A phosphopeptide with two S/T and one phosphate has isobaric positional isomers of identical precursor mass and identical peptide-level score; the localization is a SECOND inference decided only by site-determining fragment ions, with its own error rate (false localization rate, FLR). Target-decoy peptide FDR cannot estimate FLR: a wrong localization is the correct sequence with the mod one residue over, not a decoy sequence (Fermin 2013). A 1% peptide FDR does NOT yield a 1% site FDR -- report them separately.
3. A change in phosphopeptide abundance is NOT a change in phosphorylation (the biggest quant trap). Observed PTM signal ~ (site occupancy) x (protein abundance) x (enrichment/ionization factor), so log2FC(PTM_observed) = log2FC(occupancy) + log2FC(protein). Without a paired global (unenriched) proteome run on the SAME samples to subtract log2FC(protein), every protein-abundance change masquerades as a regulated site. Because co-regulated proteins move together, the false positives are pathway-coherent and look biologically convincing -- the worst kind of artifact. This is the entire reason MSstatsPTM exists (Kohler 2023).
4. Most identified sites have no known function. Fewer than ~5% of phosphosites are functionally annotated; a fold-change alone says nothing about regulatory relevance. Functional triage (conservation, stoichiometry, Ochoa functional score, confident kinase assignment) is a separate fourth layer on top of the quant (Ochoa 2020).

Bottom line: report THREE numbers, not one -- peptide/PSM FDR, per-site localization probability with its threshold, and an empirically estimated global FLR -- and never call a site "regulated" from a phospho-only run without protein-level adjustment.

Tool Taxonomy

Enrichment chemistry (phospho)

Method	Citation	Mechanism / bias	When
TiO2 (MOAC)	Larsen 2005	Metal-oxide Lewis-acid surface; skews mono-phospho; needs hydroxy-acid additive	General single-method depth; EasyPhos basis
Fe(III)-IMAC	Ruprecht 2015	Chelated Fe3+ coordinates phosphate; multidentate avidity skews MULTI-phospho	Hierarchical/processive signaling; the mono/multi divergence is STRONGEST here vs TiO2
Ti4+/Zr4+-IMAC	Matheron 2014	Chelated metal ION on immobilized phosphonate (NOT bulk oxide); bias vs TiO2 is SMALL	Modern automated workflows; metal identity matters more than IMAC-vs-MOAC
SIMAC (sequential)	Thingholm 2008	IMAC acidic elution = mono, basic = multi, then TiO2 on mono fraction	Recovering both populations IMAC alone biases

Naming trap: Ti4+/Zr4+-IMAC (chelated ions) is DIFFERENT chemistry from TiO2/ZrO2 (bulk oxide). Glycolic acid is the modern additive standard (load 80% ACN / 5% TFA / 0.1 M glycolic acid).

Other-PTM enrichment and identity traps

PTM	Reagent / mass	Citation	Headline trap
Ubiquitin (diGly, K-GG, +114.0429)	Anti-K-GG antibody	Xu 2010; Kim 2011	NOT ubiquitin-specific: K-GG = ubiquitin + NEDD8 (~6% at basal) + ISG15 (rises under interferon). UbiSite (Akimov 2018) is the ubiquitin-specific alternative
Ubiquitin alkylation artifact	use chloroacetamide	Nielsen 2008	Iodoacetamide creates a +114.0429 lysine adduct mimicking ubiquitination; chloroacetamide does not
Acetyl-K (+42.0106)	Anti-acetyllysine cocktail	Svinkina 2015	Isobaric with trimethyl +42.0470 (0.0364 Da, needs high-res); acetyl blocks trypsin -> allow >=4 missed cleavages
Glyco N-linked	PNGase F (released) or intact	Riley 2021	Released loses the glycan; N->D tag +0.984 is isobaric with deamidation -- use PNGase F in H2-18O (+2.988) to disambiguate; N-X-S/T (X!=Pro) sequon is necessary not sufficient

Localization scoring

Tool	Citation	Mechanism	Note
Ascore	Beausoleil 2006	Cumulative binomial of site-determining ions; DIFFERENCE between best and 2nd-best localization, peak-depth sweep	Ascore >=19 ~ p 0.01 PAIRWISE per-PSM, NOT a dataset FLR
PhosphoRS / ptmRS	Taus 2011	Per-isomer cumulative binomial, tolerance-aware (correct for high-res), per-site probs sum to 100%	In Proteome Discoverer
PTMProphet	Shteynberg (TPP)	EM/Bayesian mixture; per-site probs combinable across PSMs to a global FLR	TPP/FragPipe
MaxQuant Localization prob	Cox/Mann (Andromeda)	Normalized posterior on the site (fixed peak depth)	column Localization prob; >=0.75 = class I
DIA localization	Bekker-Jensen 2020	XIC peak-shape correlation substitutes for missing precursor isolation	Spectronaut EG.PTMLocalizationProbabilities; DIA-NN PTM.Site.Confidence
DeepFLR	Zong 2023	Deep-learning spectrum predictor + target-decoy FLR	SOTA direction; DDA + DIA

Site-FDR / FLR (the layer most pipelines skip)

Tool	Citation	Mechanism
LuciPHOr	Fermin 2013 (MCP)	Decoy localizations on non-modifiable residues; rate decoys win = empirical FLR
LuciPHOr2	Fermin 2015 (Bioinformatics)	Generic-PTM successor (do NOT swap the two journals)
Decoy amino-acid FLR	Ramsbottom/Jones 2022	Add a non-modifiable residue to the candidate set; global FLR ~ decoy-site-hits / target-site-hits, frequency-corrected

Kinase-activity inference

Tool	Citation	Mechanism	Limitation
KSEA	Casado 2013	z-score of a kinase's substrate fold-changes	sqrt(m) favors well-annotated kinases; inherits PhosphoSitePlus bias
PTM-SEA / PTMsigDB	Krug 2019	ssGSEA2.0 on site-level +/-7 flanking-sequence signatures	robust to isoform drift; PERT signatures score "looks like EGF stim"
RoKAI	Yilmaz 2021	Network-smooth profiles before z-score so unobserved sites borrow neighbor signal	attacks missingness; feeds KSEA

Benchmark result (Mueller-Dott 2025): across ~19 methods, simple z-score (KSEA/RoKAI) matched or beat sophisticated methods. Performance is PRIOR-limited, not algorithm-limited; all methods inherit PhosphoSitePlus curation bias toward CK2/CDK1/PKA/MAPK, and the dark kinome is structurally invisible. Spend effort on the substrate prior, not the estimator.

Decision Tree by Scenario

Scenario	Recommended	Why
Phospho-only run, want regulated sites	Acquire a PAIRED global proteome -> MSstatsPTM groupComparisonPTM -> require significance in ADJUSTED.Model	Unadjusted site changes are confounded with protein abundance
No global proteome available	Report site changes as UNADJUSTED and flag the confound explicitly	Cannot separate occupancy from abundance; do not claim "regulation"
Between-method phospho difference	Suspect chemistry (TiO2 vs Fe-IMAC mono/multi bias) BEFORE biology	Enrichment is a confounded filter
Multiply-phospho peptides present	Localize per-site (Ascore/ptmRS) AND report empirical global FLR	Peptide FDR != site FDR
"Ubiquitination" sites	Confirm chloroacetamide alkylation; treat K-GG as ub + NEDD8 + ISG15; consider UbiSite	Iodoacetamide artifact + NEDD8/ISG15 confound
Which kinases moved?	KSEA or PTM-SEA with a curated prior; do not over-interpret dark-kinome silence	Prior-limited; simple z-score suffices
Motif logo from the hits	Background = experiment-matched S/T/Y from the identified proteins (NOT whole proteome)	Whole-proteome background rediscovers disordered-region composition bias

Default when uncertain: localize with the search engine's probability (class I >=0.75), expand MaxQuant multiplicity, run MSstatsPTM with a paired global proteome, and call only ADJUSTED.Model hits regulated.

Expand the MaxQuant Site Table Before Any Quant

Goal: Produce a long, multiplicity-resolved, class-I-filtered phosphosite intensity matrix from Phospho (STY)Sites.txt.

Approach: Each site row spreads its quant across Intensity___1/___2/___3 (singly/doubly/triply-phospho forms, THREE underscores); the collapsed base Intensity mixes phospho-states and can fake dephosphorylation. Drop Reverse/contaminant, filter Localization prob, then melt the per-multiplicity columns into rows.

import pandas as pd
import numpy as np

# Filename has a SPACE in the modification name; accept either form.
phospho = pd.read_csv('Phospho (STY)Sites.txt', sep='\t', low_memory=False)

# Newer MaxQuant uses 'Potential contaminant'; older uses 'Contaminant'.
contaminant_col = 'Potential contaminant' if 'Potential contaminant' in phospho.columns else 'Contaminant'
phospho = phospho[(phospho['Reverse'] != '+') & (phospho[contaminant_col] != '+')]

CLASS_I_PROB = 0.75  # Olsen 2006 class-I convention; comparability standard, not a calibrated FLR
phospho = phospho[phospho['Localization prob'] >= CLASS_I_PROB].copy()

gene = phospho['Gene names'].where(phospho['Gene names'].notna(), phospho['Protein'])
phospho['site_id'] = gene.str.split(';').str[0] + '_' + phospho['Amino acid'] + phospho['Position'].astype(int).astype(str)

# Multiplicity columns carry THREE underscores: collapsing them mixes phospho-states.
mult_cols = [c for c in phospho.columns if '___' in c and c.split('___')[-1] in {'1', '2', }  c.startswith()]
long = phospho.melt(id_vars=[, , , ], value_vars=mult_cols, var_name=, value_name=)
long[] = long[]..split().[-]
long[] = long[]..replace(, , regex=)..replace(, , regex=)
long = long[long[] > ]
long[] = np.log2(long[])

Protein-Level Adjustment with MSstatsPTM

Goal: Decide whether each site change is real after subtracting the matched protein-abundance change.

Approach: MSstatsPTM carries TWO datasets -- a PTM dataset (enriched) and a PROTEIN dataset (global/unenriched). groupComparisonPTM fits independent linear models to each and returns a list of THREE: PTM.Model (unadjusted), PROTEIN.Model, and ADJUSTED.Model. The adjustment is dFC_adj = dFC_PTM - dFC_protein with SE_adj = sqrt(SE_PTM^2 + SE_protein^2), so adjustment ADDS uncertainty -- a site can be significant unadjusted yet lose significance after adjustment. A confident regulation call requires significance in ADJUSTED.Model.

library(MSstatsPTM)

# Converters are <Tool>toMSstatsPTMFormat and return a list with $PTM and $PROTEIN.
# MaxQtoMSstatsPTMFormat reads the MaxQuant 'evidence.txt' (NOT the Phospho (STY)Sites
# table -- the pandas multiplicity-expansion above is a SEPARATE workflow); the FASTA maps
# peptides back to site coordinates. Supply BOTH the enriched evidence and the global
# proteinGroups; without the protein dataset there is nothing to adjust against.
# Arg-name note: the FASTA argument is `fasta_path` in current MSstatsPTM; older builds may
# differ -- run `?MaxQtoMSstatsPTMFormat` to confirm before relying on it.
input <- MaxQtoMSstatsPTMFormat(
  evidence = read.table('evidence.txt', sep = '\t', header = TRUE, quote = ''),
  annotation = read.csv('annotation_ptm.csv'),
  fasta_path = 'uniprot_human.fasta',
  fasta_protein_name = 'uniprot_ac',
  proteinGroups = read.table('proteinGroups.txt', sep = '\t', header = TRUE   
  annotation_protein  read.csv
  mod_id  
  which_proteinid_ptm  
  use_unmod_peptides  


summarized  dataSummarizationPTMinput use_log_file  



result  groupComparisonPTMsummarized data.type  


adjusted  resultADJUSTED.Model
regulated  adjustedadjustedadj.pvalue  adjustedadj.pvalue    adjustedlog2FC   

Motif Analysis with the Correct Background

Goal: Find kinase/writer motifs around the modified residue without rediscovering amino-acid composition bias.

Approach: Use the Sequence window (+/-15 residues, 31-mer) MaxQuant already provides, centered on the site. The background MUST be an experiment-matched S/T/Y set drawn from the identified proteins (or a central-residue-preserving shuffle), NOT the whole proteome or IUPAC-random -- those just report the composition of phospho-rich disordered regions. motif-x and MoMo p-values are only valid when the background is built this way.

from collections import Counter

# 'Sequence window' is a 31-mer (+/-15) centered on the modified residue.
WINDOW_HALF = 7  # +/-7 flanking is the standard kinase-motif window
foreground = [w[15 - WINDOW_HALF: 16 + WINDOW_HALF] for w in confident['Sequence window'].dropna() if len(w) >= 31]

# Background: same-residue windows from the matched dataset, NOT the whole proteome.
def position_frequencies(windows):
    counts = {i: Counter() for i in range(-WINDOW_HALF, WINDOW_HALF + 1)}
    for w in windows:
        for offset, aa in zip(range(-WINDOW_HALF, WINDOW_HALF + 1), w):
            if aa not in '_X':
                counts[offset][aa] += 1
    return counts

For a publication-grade enrichment logo, hand the foreground and a matched background to a dedicated tool (motif-x / MoMo) and render with data-visualization/sequence-logos.

A Note on Home-Grown Ascore

The function below is an ILLUSTRATIVE approximation, NOT real Ascore. Real Ascore (Beausoleil 2006) competes the best localization against the second-best, sweeps peak depth 1-10 per 100 Th, and restricts to site-determining ions -- none of which this captures. Use the search engine's own localization probability (MaxQuant Localization prob, ptmRS, PTMProphet) for real work, or pyOpenMS AScore (introspect the exact API before relying on it). The home-grown form is here only to show the binomial intuition.

import numpy as np
from scipy.stats import binom

def illustrative_localization_score(matched_site_ions, total_ions, depth_p=0.04):
    '''Binomial intuition only; NOT Ascore (no best-vs-second competition or depth sweep).'''
    if total_ions == 0 or matched_site_ions == 0:
        return 0.0
    p_random = 1 - binom.cdf(matched_site_ions - 1, total_ions, depth_p)
    return -10 * np.log10(p_random) if p_random > 0 else 100.0

Per-Method Failure Modes

Skipping protein-level adjustment

Trigger: Differential testing on a phospho-only run with no paired global proteome. Mechanism: log2FC(PTM_observed) = log2FC(occupancy) + log2FC(protein); the two terms are inseparable. Symptom: Pathway-coherent "regulated sites" that are pure protein-abundance changes (cyclins/histones in cell cycle, stabilized substrates under drug). Fix: Run a matched global proteome and adjust via MSstatsPTM; route the protein-level quant to quantification.

Collapsing the MaxQuant multiplicity

Trigger: Quantifying on base Intensity instead of Intensity___1/___2/___3. Mechanism: The collapsed column mixes singly/doubly/triply-phospho forms of the same site. Symptom: The singly-phospho form dropping as a neighbor gets phosphorylated reads as dephosphorylation. Fix: Expand multiplicity to long form (Perseus "Expand site table" or the melt above) before any stats.

Treating identification as localization

Trigger: Reporting sites at peptide FDR without a localization threshold. Mechanism: Isobaric positional isomers share precursor mass and peptide score; CID/ion-trap neutral loss (-98 Da) starves site-determining ions. Symptom: A 1% peptide FDR result with a much higher true site error. Fix: Filter localization probability (class I >=0.75), report an empirical global FLR (LuciPHOr/DeepFLR), prefer HCD/EThcD.

diGly read as ubiquitin

Trigger: Calling the K-GG proteome "ubiquitination". Mechanism: NEDD8 and ISG15 share the LRLRGG C-terminus and leave the identical +114.0429 remnant; iodoacetamide adds a fourth source. Symptom: Inflated/false ubiquitin sites, worst under interferon (ISG15) or with iodoacetamide. Fix: Chloroacetamide alkylation; treat K-GG as ub+NEDD8+ISG15; use UbiSite for ubiquitin-specific mapping.

Motif logo against the wrong background

Trigger: Whole-proteome or IUPAC-random background. Mechanism: Phosphosites sit in disordered, Ser/Pro/acidic-rich regions; that composition dominates the enrichment. Symptom: "Enriched" proline/serine motifs that are region bias, not kinase preference. Fix: Experiment-matched S/T/Y background or central-residue-preserving shuffle (MoMo default).

Over-reading kinase-activity output

Trigger: Naming the top KSEA/atlas kinase as the responsible enzyme. Mechanism: Substrate priors are PhosphoSitePlus-curated (CK2/CDK1/PKA/MAPK heavy); atlas hits are biochemical preference ignoring expression/localization/timing. Symptom: Always-the-usual-suspects kinase lists; dark-kinome activity invisible. Fix: Use a curated prior, report z-scores with their substrate counts, do not infer absence from silence.

Quantitative Thresholds

Threshold	Source	Rationale
Localization prob class I >= 0.75	Olsen 2006	Best site holds >3x the posterior of all alternatives (single-phospho); a comparability standard, not a calibrated error rate
Class II 0.5-0.75; class III 0.25-0.5	Olsen 2006	Partial / poor localization
Ascore >= 19 (p~0.01); loose >13	Beausoleil 2006	Pairwise per-PSM best-vs-next confidence; NOT a dataset FLR
DIA directDIA localization >= 0.99	Bekker-Jensen 2020	Stricter than library-based (0.75) to match DDA error rates
DIA-NN site matrix 0.90 / 0.99	DIA-NN docs	phosphosites_90/99.tsv; class-I-equivalent stringency is HIGHER than MaxQuant 0.75
Acetyl missed cleavages >= 4	--	Acetyl-K blocks trypsin; pair LysC + trypsin
Kinase atlas motif match >= 90th percentile	Johnson 2023	Strong motif preference, NOT proof the kinase acted
Report peptide FDR and site FLR separately	Fermin 2013	1% peptide FDR != 1% site FDR; true site error is typically several-fold higher

Common Errors

Error / symptom	Cause	Solution
FileNotFoundError on the sites table	Filename has a SPACE: Phospho (STY)Sites.txt	Accept either spaced or no-space form
Apparent dephosphorylation that is not real	Quantified base Intensity, mixing multiplicities	Use Intensity___1/___2/___3 (three underscores)
KeyError / NaN on Gene names	Column is FASTA-dependent, absent without gene annotation	Guard with .notna() and fall back to Protein
All sites "regulated" and pathway-coherent	No protein-level adjustment	Require significance in MSstatsPTM ADJUSTED.Model
PTM.Q.Value / PhosphoSite not found (DIA-NN)	Those columns do not exist	Use PTM.Site.Confidence and Site.Occupancy.Probabilities
False "ubiquitination" sites	Iodoacetamide +114.0429 lysine artifact	Alkylate with chloroacetamide
Acetyl confused with trimethyl	+42.0106 vs +42.0470 isobaric at nominal mass	Require high-res MS; check 0.0364 Da split

References

• Beausoleil SA, Villen J, Gerber SA, Rush J, Gygi SP. A probability-based approach for high-throughput protein phosphorylation analysis and site localization. Nat Biotechnol 2006;24(10):1285-1292.
• Taus T, Kocher T, Pichler P, et al. Universal and confident phosphorylation site localization using phosphoRS. J Proteome Res 2011;10(12):5354-5362.
• Olsen JV, Blagoev B, Gnad F, et al. Global, in vivo, and site-specific phosphorylation dynamics in signaling networks. Cell 2006;127(3):635-648.
• Fermin D, Walmsley SJ, Gingras AC, Choi H, Nesvizhskii AI. LuciPHOr: algorithm for phosphorylation site localization with false localization rate estimation using modified target-decoy approach. Mol Cell Proteomics 2013;12(11):3409-3419.
• Fermin D, Avtonomov D, Choi H, Nesvizhskii AI. LuciPHOr2: site localization of generic PTMs from tandem mass spectrometry data. Bioinformatics 2015;31(7):1141-1143.
• Bekker-Jensen DB, Bernhardt OM, Hogrebe A, et al. Rapid and site-specific deep phosphoproteome profiling by data-independent acquisition without the need for spectral libraries. Nat Commun 2020;11:787.
• Kohler D, Tsai TH, Verschueren E, et al. MSstatsPTM: Statistical Relative Quantification of Posttranslational Modifications in Bottom-Up Mass Spectrometry-Based Proteomics. Mol Cell Proteomics 2023;22(1):100477.
• Ochoa D, Jarnuczak AF, Vieitez C, et al. The functional landscape of the human phosphoproteome. Nat Biotechnol 2020;38(3):365-373.
• Casado P, Rodriguez-Prados JC, Cosulich SC, et al. Kinase-Substrate Enrichment Analysis Provides Insights into the Heterogeneity of Signaling Pathway Activation in Leukemia Cells. Sci Signal 2013;6(268):rs6.
• Krug K, Mertins P, Zhang B, et al. A Curated Resource for Phosphosite-specific Signature Analysis. Mol Cell Proteomics 2019;18(3):576-593.
• Yilmaz S, Ayati M, Schlatzer D, et al. Robust inference of kinase activity using functional networks. Nat Commun 2021;12:1177.
• Larsen MR, Thingholm TE, Jensen ON, Roepstorff P, Jorgensen TJD. Highly selective enrichment of phosphorylated peptides from peptide mixtures using titanium dioxide microcolumns. Mol Cell Proteomics 2005;4(7):873-886.
• Ruprecht B, Koch H, Medard G, et al. Comprehensive and reproducible phosphopeptide enrichment using iron immobilized metal ion affinity chromatography (Fe-IMAC) columns. Mol Cell Proteomics 2015;14(1):205-215.
• Matheron L, van den Toorn H, Heck AJR, Mohammed S. Characterization of biases in phosphopeptide enrichment by Ti(IV)-IMAC and TiO2 using a massive synthetic library and human cell digests. Anal Chem 2014;86(16):8312-8320.
• Thingholm TE, Jensen ON, Robinson PJ, Larsen MR. SIMAC (sequential elution from IMAC), a phosphoproteomics strategy for the rapid separation of monophosphorylated from multiply phosphorylated peptides. 2008;7(4):661-671.

Related Skills

• peptide-identification - Identify modified peptides and run open/variable-mod search
• quantification - Underlying protein-level quant feeding the MSstatsPTM PROTEIN dataset
• differential-abundance - Moderated testing on the protein-level intensity matrix
• pathway-analysis/gsea - Enrichment scoring of regulated-site protein lists and PTM-SEA-style signatures
• data-visualization/sequence-logos - Render motif logos from the foreground/background windows