Get Your Custom Quote

Online Inquiry

Unlock protein degradation insights with Creative Proteomics’ targeted assays, delivering accurate, proteome-wide analysis to advance your research efficiently.

Get Your Custom Quote

Targeted Protein Degradation (TPD) Services

We provide targeted protein degradation analysis services powered by quantitative proteomics and high-resolution mass spectrometry, helping researchers accurately measure degradation efficiency, selectivity, and proteome-wide impact. Our workflows are built on well-established proteomics methodologies, delivering reliable, reproducible, and interpretable results that support confident decision-making across degrader research projects.

  • Quantitative degradation profiling to measure target protein loss across conditions and time points precisely
  • Proteome-wide selectivity assessment to reveal unintended protein changes beyond the primary target
  • High-confidence mass spectrometry analysis supported by robust quality control and statistical validation
  • Expert-driven data interpretation grounded in extensive experience with complex proteomics datasets
Creative Proteomics' TPD services.

What is Targeted Protein Degradation (TPD)?

Targeted Protein Degradation (TPD) refers to approaches that leverage endogenous cellular degradation pathways to reduce the abundance of specific proteins. Instead of blocking protein activity, TPD strategies aim to remove the protein itself, enabling regulation of scaffolding proteins, multi-domain complexes, and other challenging targets.

Most TPD approaches rely on intracellular degradation systems, such as ubiquitin-dependent proteolysis or lysosome-mediated pathways. Because degradation is an event-driven process, assays must directly measure protein abundance changes over time, rather than relying solely on downstream functional readouts.

Proteomics technologies for TPD.

Figure 1. Comprehensive proteomics technologies for advancing targeted protein degradation (Sathe G, et al., 2023).

Targeted Protein Degradation Testing Strategies

TPD assay strategies can be broadly grouped according to project stage:

Advantages of Quantitative Proteomics in TPD

Mass Spectrometry-Based Proteomics for TPD

Discovery proteomics

Best for broad off-target discovery. Works well with many samples and offers high dynamic range. Typical deliverables: protein-level fold changes, volcano plots, pathway enrichment.

Multiplexed quantitative proteomics

Isobaric labeling enables multiplexing of samples to analyze time courses or dose responses within a single run. It increases throughput and reduces run-to-run variability but requires careful correction for ratio compression.

Targeted proteomics and absolute quantitation

Once targets are defined, targeted MS yields high sensitivity and precise quantitation suitable for translational PD assays. Targeted assays are necessary for low-abundance proteins or clinical sample work.

Our Mass Spectrometry Workflows for Degrader Profiling

Creative Proteomics' TPD profiling workflows.

Selecting the Right Service Package

Our service packages can support TPD research across stages:

How Creative Proteomics Supports TPD Research

Our services provide integrated solutions for TPD studies:

Sample Requirements for TPD Assays

Sample type Typical use case Minimum input (practical) Recommended input Storage / prep
Cultured cells (lysate) Screening, High-Sensitivity Protein Tagging Technology, TR-Fluorescence Energy Transfer, discovery MS 0.5–1 ×106 cells 2–5 ×106 cells per condition Wash, pellet, snap-freeze; store −80°C; avoid multiple freeze–thaw
Tissue (fresh/frozen) Discovery MS, TMT, PRM 2–5 mg 10–20 mg Snap-freeze in liquid N2; store −80°C; homogenize in appropriate buffer
Plasma / serum Plasma proteomics, PK/PD, biomarker testing 20–50 µL 100–200 µL Collect EDTA/heparin; separate plasma quickly; store −80°C

Application Areas Supported by TPD Proteomics

Why Choose Creative Proteomics for TPD Services

FAQ

Q1: What biological systems do TPD approaches leverage?

A1: Most TPD strategies exploit the ubiquitin-proteasome system, but some emerging approaches also involve lysosome-mediated degradation pathways.

Q2: How does proteomic profiling distinguish on- and off-target effects?

A2: By comparing protein abundance between control and degrader-treated samples across the proteome, statistical analysis can identify proteins that are significantly degraded in a treatment-specific manner.

Q3: How do mass spectrometry and structural methods complement each other in TPD research?

A3: Proteomics reveals functional protein abundance changes, while structural methods like modelling or interaction studies help interpret ternary complex formation critical for degraders.

Q4: What are common pitfalls in TPD proteomics?

A4: Challenges include low abundance targets, incomplete proteome coverage, batch effects, and distinguishing degradation from protein synthesis inhibition.

Demo

Demo: Proteome-Wide Discovery of Degradable Proteins Using Bifunctional Molecules

large-scale TMT proteomics screen of a bifunctional library to identify proteins that can be driven to degradation; demonstrates how proteomics-first screening can expand the set of degrader-tractable proteins.

Chemical proteomic for the degradable proteome.

Figure 2. Chemical proteomic strategy to interrogate the degradable proteome (Forrest I, et al., 2025).

Case Study

Case: Degradome analysis to identify direct protein substrates of small-molecule degraders

Background

Mass spectrometry–based proteomics can measure protein abundance and ubiquitination at scale, but conventional time-course proteomics alone may conflate primary degradation with downstream effects caused by target loss. Jochem et al. develop a mass-spec workflow focused on isolating direct degradation events while excluding confounding transcriptional or translational changes.

Purpose

The study's objective was to create and validate a proteomics method that selectively quantifies proteins undergoing direct degradation after treatment with small-molecule degraders, thereby enabling confident identification of on-target substrates and distinguishing them from secondary proteome responses. The method aims to be broadly applicable across degrader modalities and to produce high-confidence substrate lists for downstream mechanistic studies.

Methods

  • Metabolic pulse labelling: Cells are pulse-labelled with an azidohomoalanine (AHA) or by SILAC-based approaches to mark newly synthesized proteins prior to/after degrader treatment.
  • Click chemistry enrichment: AHA-labelled proteins (or isotopically labelled channels) are selectively enriched via click chemistry, isolating pools that reflect nascent synthesis and allowing subtraction of synthesis-driven changes.
  • Quantitative LC–MS/MS: Enriched samples are analyzed by high-resolution LC–MS/MS with isobaric or metabolic labelling quantitation to compare treated vs control conditions at defined time points. 
  • Orthogonal validation: The approach is benchmarked against known degraders and validated using proteasome inhibitors and genetic perturbations when appropriate.

Results

  • Method performance: DegMS robustly identifies directly degraded substrates and reduces false positives that would arise from transcriptional/translational down-regulation.
  • Application to known degraders: When applied to established degrader molecules, the method recapitulated known primary substrates and revealed additional, previously unrecognized direct targets in the same datasets. 
  • Case studies / mechanistic insight: The authors demonstrate that combining DegMS with proteasome inhibition and orthogonal assays  helps confirm that identified hits are processed via the expected degradation pathway.
Results of degradome proteomics by DegMS.

Figure 3. Degradome proteomics by DegMS.

Results of benchmarking of degradome proteomics.

Figure 4. Benchmarking of degradome proteomics by DegMS.

Results of proteome response.

Figure 5. Proteome response to CC-885 treatment.

Conclusion

DegMS is a scalable, MS-based strategy that selectively captures proteins undergoing direct degradation in response to small-molecule degraders while excluding confounding synthesis changes. This yields higher-confidence substrate lists and helps distinguish primary molecular targets from downstream proteome effects. The paper demonstrates the method's applicability across different degrader chemistries and provides deposited datasets for community reuse, making it a practical template for labs and CROs that need mechanistic substrate identification in degrader projects.

Related Services

References

For research purposes only, not intended for clinical diagnosis, treatment, or individual health assessments.

Tell Us About Your Project