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.
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.
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
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.
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.
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).
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.
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
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
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', '3'} and c.startswith('Intensity')]
long = phospho.melt(id_vars=['site_id', 'Amino acid', 'Position', 'Localization prob'], value_vars=mult_cols, var_name='run_multiplicity', value_name='intensity')
long['multiplicity'] = long['run_multiplicity'].str.split('___').str[-1]
long['run'] = long['run_multiplicity'].str.replace(r'___[123]$', '', regex=True).str.replace('Intensity ', '', regex=False)
long = long[long['intensity'] > 0]
long['log2_intensity'] = np.log2(long['intensity'])
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 $PROTEIN.# MaxQtoMSstatsPTMFormat reads the MaxQuant 'evidence.txt' (NOT the Phospho (STY)Sites# table -- the pandas multiplicity-expansion above is a SEPARATE workflow); the FASTA maps# peptides back to site coordinates. Supply BOTH the enriched evidence and the global# proteinGroups; without the protein dataset there is nothing to adjust against.# Arg-name note: the FASTA argument is `fasta_path` in current MSstatsPTM; older builds may# differ -- run `?MaxQtoMSstatsPTMFormat` to confirm before relying on it.
input <- MaxQtoMSstatsPTMFormat(
evidence = read.table('evidence.txt', sep ='\t', header =TRUE,quote=''),
annotation = read.csv('annotation_ptm.csv'),
fasta_path ='uniprot_human.fasta',
fasta_protein_name ='uniprot_ac',
proteinGroups = read.table('proteinGroups.txt', sep ='\t', header =TRUE,quote=''),
annotation_protein = read.csv('annotation_protein.csv'),
mod_id ='\\(Phospho \\(STY\\)\\)',
which_proteinid_ptm ='Proteins',
use_unmod_peptides =FALSE)
summarized <- dataSummarizationPTM(input, use_log_file =FALSE)# LabelFree run: data.type = 'LF' (use 'TMT' for isobaric); contrast.matrix defaults to# full pairwise. groupComparisonPTM has NO `model` argument -- it always fits independent# PTM and PROTEIN models, then adjusts.
result <- groupComparisonPTM(summarized, data.type ='LF')# Three models; the adjusted one is the deliverable.
adjusted <- result$ADJUSTED.Model
regulated <- adjusted[!is.na(adjusted$adj.pvalue)& adjusted$adj.pvalue <0.05&abs(adjusted$log2FC)>1,]# How much of each call was protein-driven: compare PTM.Model vs ADJUSTED.Model.
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() iflen(w) >= 31]
# Background: same-residue windows from the matched dataset, NOT the whole proteome.defposition_frequencies(windows):
counts = {i: Counter() for i inrange(-WINDOW_HALF, WINDOW_HALF + 1)}
for w in windows:
for offset, aa inzip(range(-WINDOW_HALF, WINDOW_HALF + 1), w):
if aa notin'_X':
counts[offset][aa] += 1return 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
defillustrative_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 == 0or matched_site_ions == 0:
return0.0
p_random = 1 - binom.cdf(matched_site_ions - 1, total_ions, depth_p)
return -10 * np.log10(p_random) if p_random > 0else100.0
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. Mol Cell Proteomics 2008;7(4):661-671.
Svinkina T, Gu H, Silva JC, et al. Deep, Quantitative Coverage of the Lysine Acetylome Using Novel Anti-acetyl-lysine Antibodies and an Optimized Proteomic Workflow. Mol Cell Proteomics 2015;14(9):2429-2440.
Xu G, Paige JS, Jaffrey SR. Global analysis of lysine ubiquitination by ubiquitin remnant immunoaffinity profiling. Nat Biotechnol 2010;28(8):868-873.
Kim W, Bennett EJ, Huttlin EL, et al. Systematic and Quantitative Assessment of the Ubiquitin-Modified Proteome. Mol Cell 2011;44(2):325-340.
Nielsen ML, Vermeulen M, Bonaldi T, Cox J, Moroder L, Mann M. Iodoacetamide-induced artifact mimics ubiquitination in mass spectrometry. Nat Methods 2008;5(6):459-460.
Akimov V, Barrio-Hernandez I, Hansen SVF, et al. UbiSite approach for comprehensive mapping of lysine and N-terminal ubiquitination sites. Nat Struct Mol Biol 2018;25(7):631-640.
Riley NM, Bertozzi CR, Pitteri SJ. A Pragmatic Guide to Enrichment Strategies for Mass Spectrometry-Based Glycoproteomics. Mol Cell Proteomics 2021;20:100029.
Johnson JL, Yaron TM, Huntsman EM, et al. An atlas of substrate specificities for the human serine/threonine kinome. Nature 2023;613(7945):759-766.
Mueller-Dott S, Jaehnig EJ, et al. Comprehensive evaluation of phosphoproteomic-based kinase activity inference. Nat Commun 2025;16:4771.
Zong Y, Wang Y, Yang Y, et al. DeepFLR facilitates false localization rate control in phosphoproteomics. Nat Commun 2023;14:2269.
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
