Global PTM Profiling Service — Unbiased, Proteome-Wide Post-Translational Modification Discovery

Use Global PTM Profiling When:

• You are studying an organism, cell type, or disease state where the PTM landscape has not been characterized and you do not know which modifications are predominant or regulated.
• You have a biological system that undergoes major perturbation (drug treatment, stress, differentiation, infection) and you want an unbiased view of the full PTM response — not just the modification type you expected to change.
• You need a publication-grade PTM dataset that demonstrates breadth of modification coverage — not just phosphoproteomics, but a multi-modification characterization of your system.
• You want to identify novel PTM types or modification combinations on specific proteins of interest, including unexpected modifications introduced by metabolic conditions, drug treatment, or cellular stress.
• You are designing a targeted follow-up experiment (DIA quantification, PRM verification) and need a comprehensive site list to define the target universe — because targeted experiments can only confirm what you already know to look for.

What Global Profiling Reveals That Single-PTM Workflows Miss:

• Co-modification patterns: proteins carrying multiple modification types simultaneously — the combinatorial PTM code on histones, transcription factors, and signaling regulators that single-PTM enrichments cannot detect because each enrichment is performed separately on separate samples.
• Unexpected modification types: novel acylation marks, non-enzymatic modifications (glycation, carbonylation, deamidation), drug-induced covalent adducts, or metabolite-derived modifications that no single-PTM workflow would identify because no enrichment resin exists for them.
• Modification crosstalk networks: by identifying multiple modification types on the same proteins in the same experiment, global profiling directly enables PTM crosstalk analysis — identifying regulatory relationships between phosphorylation and ubiquitylation, acetylation and methylation, or any other modification pair on shared substrates.
• Modification type distribution: the relative frequency of different modification classes across the proteome — revealing which modifications dominate the regulatory landscape of your biological system, and where to focus modification-specific deep-dive experiments for maximum biological yield.

Global Profiling vs. Single-PTM Deep Profiling — Choosing the Right Approach:

Strategy 1 — Open-Search PTM Discovery

Unbiased, library-free identification of known and unanticipated modifications in unenriched samples using wide precursor mass tolerance or tag-based open search algorithms (FragPipe/MSFragger, MetaMorpheus G-PTM-D, PTM-Shepherd). The search tolerates a mass window of ±500–1000 Da around each peptide, identifying all mass additions consistent with known or novel modifications at each residue.

What it detects: all enzymatic PTMs present at sufficient abundance in the unenriched sample — phosphorylation, acetylation, methylation, deamidation, oxidation, carbamylation, glycation, and modification types not anticipated in a conventional closed search. Also identifies sample preparation artifacts (incomplete alkylation, oxidized methionine) that affect data quality.

Depth: reflects the modification stoichiometry distribution in the unenriched sample — high-stoichiometry modifications (abundant acetylation on histones, constitutive phosphorylation on abundant proteins) are detected confidently; low-stoichiometry modifications require enrichment for detection. Open search is best understood as a modification survey tool, not a deep site-mapping tool.

Software: MSFragger (FragPipe suite) open search; PTM-Shepherd for modification type summarization and site annotation; MetaMorpheus G-PTM-D for combined curated + novel modification discovery.

Link to dedicated service: Open-Search PTM Discovery Service →

Strategy 2 — Sequential Multi-PTM Enrichment

Two to four modification-specific enrichment steps applied sequentially to the same tryptic digest — each step capturing a different modification class, enriching the corresponding modified peptides for dedicated LC-MS/MS analysis. This strategy maximizes depth for each individual modification type while maintaining the sample identity across modification classes, enabling direct cross-PTM comparison from the same biological sample.

Standard panel configurations:

• Core signaling panel: Phospho (IMAC) + Ubiquitylation (diGLY IP) — the most commonly requested combination for drug MoA and signal transduction studies.
• Epigenetic panel: Phospho + Acetyl (pan-Ac-Lys antibody) + Methyl (pan-me-Lys/Arg antibody) — for chromatin biology and histone modification research.
• Metabolic acyl panel: Acetyl + Crotonyl + Succinyl + Lactyl — for metabolic reprogramming and Warburg effect research.
• Multi-PTM redox panel: Phospho + Cysteine redox (differential alkylation) + Acetyl — for oxidative stress and redox signaling studies (see also Redox PTM Proteomics).
• Custom panel: any combination of 2–4 modification types with compatible enrichment chemistries — discussed during project consultation.

Depth per PTM type: comparable to dedicated single-PTM workflows — 5,000–20,000+ phosphosites, 3,000–10,000 acetylation sites, 5,000–15,000 K-ε-GG ubiquitylation sites — from the same starting digest.

Strategy 3 — Unenriched Deep Proteome + PTM Profiling

High-depth DIA or TMT-based total proteome analysis from an unenriched digest, combined with variable modification database searching that includes all major PTM types as variable modifications. This approach identifies high-stoichiometry modifications (constitutive phosphorylation on abundant proteins, N-terminal acetylation, abundant glycosylation, histone marks) directly from the total proteome data without a dedicated enrichment step.

Advantage: provides both protein abundance and modification data from the same experiment — no additional sample is consumed for modification analysis. Particularly valuable for low-input samples (single cells, rare biopsies, microdissected tissues) where sample quantity does not permit sequential enrichment.

Limitation: detection is limited to modifications present at ≥1–5% occupancy on proteins that are identified at sufficient depth in the unenriched proteome. Low-stoichiometry modifications on low-abundance proteins will not be detected without enrichment.

Instruments: Bruker timsTOF Pro (diaPASEF) or Thermo Orbitrap Fusion Lumos for maximum unenriched proteome depth; EThcD fragmentation for labile modifications; variable modification search in Spectronaut (DIA) or MaxQuant (DDA).

Standard Global PTM Profiling

Open-search discovery analysis of a tryptic digest using MSFragger or MetaMorpheus G-PTM-D — identifying all detectable modification types and their sites in the unenriched sample. Includes PTM-Shepherd post-processing for modification type summarization, site annotation against PhosphoSitePlus and UniMod, and a comprehensive modification landscape report. Suitable as a first-pass survey experiment preceding targeted single-PTM enrichment studies.

Input: ≥100 μg total protein (cells or tissue); ≥200 μL plasma/serum (depleted recommended).

Deliverables: raw data, modified peptide identification table (all modification types), modification type distribution summary, site annotation against PTM databases, modification co-occurrence matrix, bioinformatics figures, methods text, project report.

Multi-PTM Sequential Enrichment Profiling

Two to four modification-specific enrichments applied sequentially to the same protein digest — generating deep site-level data for each modification type from the same biological sample. Includes integrated cross-modification analysis: proteins regulated in two or more modification types are identified, co-modification correlation is calculated, and pathway-level network analysis annotates crosstalk between modification pairs. The most comprehensive global PTM dataset available from a single CRO engagement.

Input: ≥3–10 mg total protein per sample (varies by number of enrichment steps).

Deliverables: all single-PTM deliverables for each modification type covered, plus: cross-modification correlation matrix, co-regulated protein list, PTM crosstalk network visualization, integrated pathway enrichment across modification types, combined publication-ready figure set.

Comparative Global PTM Profiling

Global PTM Profiling applied across two or more biological conditions — treatment vs. control, disease vs. normal, or time-course samples — enabling identification of which modification types and specific sites change between conditions. Uses label-free, TMT, or SILAC quantification strategies depending on sample type and experimental design. Generates a differential modification landscape: modification types with overall regulation, proteins with regulation in multiple PTM types, and pathway-level analysis of regulated modification networks.

Best for: initial characterization of the PTM response to a drug, genetic perturbation, or disease state — before committing to single-PTM deep quantification workflows.

Input: ≥3–5 mg per sample (LFQ), ≥2 mg (TMT), ≥5 mg (SILAC); minimum 3 biological replicates per condition.

Low-Input Global PTM Profiling

Specialized global PTM Profiling workflow for rare or limited samples — FFPE tissue sections, single sorted cell populations (≥50,000 cells), laser-capture microdissected material, or precious clinical biospecimens where standard input amounts are not available. Uses SP3-based sample preparation, nanoscale LC gradients, and Bruker timsTOF Pro or Orbitrap Fusion Lumos configured for maximum sensitivity at low peptide loads. Open-search + variable modification search from unenriched digest provides breadth-limited but unbiased PTM survey data from sub-microgram protein inputs.

Protein-Specific PTM Landscape Mapping

Global PTM characterization focused on a single protein or protein complex of interest — immunoprecipitated or affinity-purified before digestion. All modification types on the captured protein are identified simultaneously by LC-MS/MS with variable modification search across all Unimod entries, providing a complete modification map of the target protein including unexpected, low-stoichiometry, or cell-state-dependent modifications that no pre-specified modification search would identify.

Applications: characterization of recombinant protein products; modification mapping of a protein of pharmacological interest; IP-MS modification characterization before functional mutagenesis studies.

Global PTM + Total Proteome Integration

Combined global PTM profiling and total proteome quantification from the same sample — providing both protein abundance and modification data in a unified project. The 5% pre-enrichment aliquot retained from each sample is analyzed by DIA or TMT proteomics, and protein abundance ratios are used to normalize modification fold-changes for abundance effects — delivering modification-intrinsic regulation data. Also enables identification of proteins where regulation occurs exclusively at the PTM level with no change in total abundance — a biologically important class of regulatory events invisible to total proteomics alone.

Step 1 — Project Design & Strategy Selection

We review your sample type, biological question, modification types of interest (if any), available input, and publication goals. Based on this, we recommend the optimal profiling strategy — open search, sequential enrichment, or integrated total proteome + PTM — and specify the enrichment panel configuration if applicable. Pre-collection handling requirements are confirmed: phosphatase inhibitors (phospho), NEM (ubiquitylomics), snap-freeze protocols (histone stability). We provide a complete sample collection guide before you collect samples.

Step 2 — Sample Preparation

Protein extraction uses matrix-appropriate lysis buffers — denaturing (8 M urea) for maximum protein solubilization; acid extraction for histone modification studies; non-denaturing for interaction-preserving applications. Reduction (DTT), alkylation (chloroacetamide), and trypsin digestion are performed under standardized conditions validated for each matrix. Peptide yield and missed cleavage rate are measured. A pre-enrichment aliquot (5%) is retained from each sample for total protein abundance quantification. For TMT projects, isobaric labeling and pooling are performed before the enrichment stage.

Step 3 — Enrichment (Strategy-Dependent)

For sequential multi-PTM enrichment: each enrichment step is applied to aliquots from the same digest in series. Order is optimized to maximize recovery: phospho IMAC is applied first (highest recovery from unenriched digest), followed by antibody-based enrichments (diGLY IP, pan-Ac-Lys, etc.), each using the post-IMAC flow-through or fresh aliquots depending on compatibility. Enrichment efficiency is monitored per step using spiked synthetic peptide standards. For open-search profiling: the unenriched digest is analyzed directly — no enrichment required.

Step 4 — LC-MS/MS Acquisition

Acquisition mode and platform are matched to the profiling strategy. For open-search discovery: DDA on Orbitrap Fusion Lumos (high MS/MS scan rate for broad PTM fragmentation coverage) with HCD at 27–30% NCE; EThcD available for labile modifications. For sequential enrichment (deep coverage per PTM): DDA (SILAC/LFQ) or DIA (large cohorts); diaPASEF on Bruker timsTOF Pro for ion-mobility-enhanced depth. For total proteome + PTM integration: DIA on Q Exactive HF-X or timsTOF Pro for the proteome fraction, DDA for the enriched PTM fractions. Instrument QC includes iRT injection, mass accuracy check, and spray stability verification before each run.

Step 5 — Integrated Bioinformatics Analysis

Open-search data is processed in FragPipe (MSFragger open search + PTM-Shepherd) and/or MetaMorpheus G-PTM-D with UniMod-curated and novel modification libraries; 1% FDR at PSM and protein level; modification localization by MSFragger site scoring. Enrichment-based data is processed in MaxQuant (SILAC/LFQ) or Proteome Discoverer (TMT) with modification-specific variable modifications, ptmRS/phosphoRS site localization. For integrated multi-PTM projects: cross-modification analysis identifies proteins present in two or more modification datasets; Pearson correlation between modification-type fold-changes is calculated per protein; PTM crosstalk networks are constructed using STRING PPI data.

Step 6 — Publication-Ready Report Generation

All analysis outputs are compiled into a structured deliverable package organized for direct use in manuscript preparation. The report includes: a modification landscape overview figure (pie chart of modification type distribution, protein-level modification frequency histogram), site confidence summary (localization probability distribution), differential regulation results per modification type (volcano plots, heatmaps), cross-modification co-regulation analysis, GO/KEGG/Reactome pathway enrichment figures, PTM crosstalk network diagram, and a complete Materials and Methods section written at publication standard — covering sample preparation, enrichment, instrument parameters, database search, quantification, and statistical analysis. All figures are provided as editable vector files (PDF or SVG) and high-resolution PNG for direct manuscript use.

Raw & Processed Data Files

• Raw LC-MS/MS data files in Thermo .raw or Bruker .d format
• FragPipe/MaxQuant/Proteome Discoverer project files with all search parameters
• Modified peptide identification table: sequence, charge state, modified residue(s), modification type, mass shift, localization probability score, spectral FDR, protein accession
• Site-level quantification matrix (if quantitative): normalized intensities or ratios across all samples and replicates
• Protein-level abundance matrix from pre-enrichment aliquot (for integrated projects)

Modification Landscape Figures

• Modification type distribution: pie or bar chart showing the number of identified sites per modification class (phosphorylation, acetylation, methylation, ubiquitylation, acylation, etc.)
• Modified amino acid frequency: bar chart of modification frequency per residue type (Ser, Thr, Tyr, Lys, Arg, Cys, Met, Asn)
• Protein-level modification frequency histogram: number of distinct modification types per protein, identifying heavily modified regulatory proteins
• Site localization probability distribution: violin or histogram confirming high-confidence site assignments
• Co-modification heatmap: proteins carrying multiple modification types, with modification type as rows and proteins as columns

Differential Analysis Results (Comparative Projects)

• Volcano plots per modification type: log2 fold-change vs. −log10(adjusted p-value), significantly regulated sites highlighted, top sites annotated
• Hierarchical clustering heatmap of significantly regulated sites across conditions
• Modification-type regulation summary: number of up/down-regulated sites per PTM class per comparison
• Overlap Venn diagram: proteins with regulated sites in two or more modification types
• Cross-modification correlation scatter plot: modification fold-changes plotted against each other for shared regulated proteins

Pathway & Network Analysis

• GO biological process, molecular function, and cellular component enrichment — bubble plots for each modification type
• KEGG and Reactome pathway enrichment — top 15 pathways per modification type, with regulated proteins mapped
• Kinase-substrate enrichment analysis (KSEA) for phospho data — kinase activity inference from regulated phosphosite substrates
• PTM crosstalk network: STRING PPI network of proteins regulated in multiple modification types, with edge color coding modification pair co-regulation
• Modification motif analysis: sequence logos of modification site flanking sequences, colored by upstream kinase or writer enzyme family

Publication-Ready Methods Section

A complete, accurate, publication-standard Materials and Methods section covering every aspect of the experiment — written at the level expected by high-impact proteomics journals (Mol Cell Proteomics, J Proteome Res, Nat Commun, Cell Reports). Covers:

• Sample preparation (lysis, reduction, alkylation, digestion, optional fractionation)
• Enrichment protocols (resin type, batch size, wash conditions, elution)
• LC-MS/MS parameters (column, gradient, instrument model, acquisition mode, resolution, AGC, fill time)
• Database search parameters (software version, database, enzyme, variable modifications, FDR thresholds, site localization algorithm)
• Quantification and normalization approach
• Statistical analysis (test type, multiple testing correction, significance thresholds)
• Bioinformatics tools (software versions, databases, visualization tools)

Comprehensive Project Report

• Executive summary of key findings per modification type
• QC metrics: peptide identification depth, FDR, enrichment efficiency, inter-replicate Pearson correlation, CV distribution
• Interpretation of modification landscape in the context of the submitted biological question
• Recommendations for downstream follow-up: priority modification types for deeper quantitative profiling, priority sites for PRM verification, suggested crosstalk relationships for functional validation
• All figures provided as editable PDF/SVG + high-resolution PNG (300 dpi minimum), formatted for direct manuscript insertion or journal figure preparation

Background & Scientific Question

Pancreatic β-cell dysfunction driven by cytokine-mediated inflammation is a central mechanism in type 1 diabetes. The regulatory response to cytokine stress in β-cells is known to involve multiple PTM layers — JAK-STAT and NF-κB signaling (phosphorylation), epigenetic reprogramming (acetylation), and oxidative stress-driven protein modification (cysteine thiol oxidation). However, these layers had predominantly been studied in isolation: separate experiments, separate samples, and separate analytical workflows — making it impossible to identify regulatory crosstalk between modification types or to determine which proteins are co-regulated at the PTM level across multiple modification classes simultaneously.

Gluth et al. from PNNL asked: can a single integrated sample preparation workflow quantify protein abundance, cysteine thiol oxidation, phosphorylation, and acetylation from the same samples in the same time-course experiment — and what co-regulatory relationships between these PTM types emerge when you do?

Methods

The study developed an integrative SP3-based workflow that applies four analytical dimensions to the same sample: (1) total protein abundance by DIA proteomics from the unenriched fraction; (2) cysteine thiol oxidation by differential alkylation with iodoacetamide (reduced cysteines) and MMTS (oxidized cysteines) followed by enrichment; (3) phosphorylation by IMAC enrichment; and (4) acetylation by pan-acetyllysine antibody enrichment.

MIN6 mouse β-cell cultures were treated with a cytokine mixture (IL-1β, IFN-γ, TNF-α) across a six-point time course (0, 1, 3, 6, 12, 24 h), with four biological replicates per time point. TMT11-plex labeling enabled all 11 time point–replicate combinations to be compared within a single LC-MS/MS run per modification type. The integrated dataset comprised protein abundance, cysteine oxidation, phosphorylation, and acetylation measurements from the same 44 samples.

Results Overview

The integrated multi-PTM workflow identified thousands of regulated events across all four analytical dimensions. Global protein abundance analysis revealed a rapid response consistent with JAK-STAT and NF-κB pathway activation within 1–3 h of cytokine treatment. Cysteine thiol oxidation showed a distinct temporal profile — peaking at 6–12 h — consistent with a secondary oxidative stress wave following initial immune activation. Phosphoproteomics identified thousands of regulated phosphosites with temporal profiles mapping to kinase activation sequences in JAK-STAT, MAPK, and NF-κB pathways. Acetylomics revealed time-dependent changes in histone and non-histone protein acetylation linked to transcriptional reprogramming during prolonged cytokine stress.

The key biological insight only accessible through integrated multi-PTM analysis: PARP14, a negative regulator of JAK-STAT signaling, was identified with multiple co-localized PTMs — phosphorylation, acetylation, and cysteine oxidation on the same protein, at different time points — suggesting intraprotein PTM crosstalk as a mechanism for fine-tuning JAK-STAT suppression during the acute-to-chronic inflammatory transition. This finding would have been invisible to any single-PTM study.

Relevance to Our Service

This study is the definitive demonstration of what integrated multi-PTM profiling delivers that single-PTM workflows structurally cannot: the identification of proteins co-regulated across multiple modification types, the temporal co-ordination between modification classes, and the discovery of functional PTM crosstalk relationships on specific regulatory proteins. The PARP14 multi-PTM finding is a prototype of the type of discovery that only emerges when phosphorylation, acetylation, and redox modifications are profiled from the same samples in the same experiment.

Our Multi-PTM Sequential Enrichment Profiling service uses the same conceptual framework — same sample, multiple enrichment steps, integrated analysis — enabling this class of discovery in your biological system. The publication-ready deliverables from our platform include all the figure types required to present integrated multi-PTM data in high-impact journals, including the cross-modification temporal heatmaps, co-regulation scatter plots, and PTM crosstalk network visualizations demonstrated in this paper.