Protein–Protein Interaction (PPI) Analysis Service

Q: How do I choose a method to detect transient or weak PPIs?

Use proximity labeling in living cells to capture neighbors within a limited radius, then corroborate priority hits with orthogonal assays; PL complements enrichment-based methods by recovering short-lived or compartment-restricted contacts.

Q: What's the practical difference between AP-MS and proximity labeling?

AP-MS enriches stable complexes under near-native conditions and excels at condition-dependent interactomes, while proximity labeling biotin-tags nearby proteins in cells to reveal weak/transient neighbors; together they provide overlapping but distinct views of an interactome.

Q: When should I use XL-MS and what does it add?

XL-MS supplies residue-pair distance restraints that localize binding interfaces and support integrative modeling; large-scale studies show PPIs can be reported at controlled PPI-FDR (for example, 1%) to improve confidence in interaction calls.

Q: SPR, BLI, MST, or ITC: which for affinity and kinetics?

SPR provides sensitive, real-time kinetics (kon/koff, KD) and is strong for small-molecule or fragment binding; BLI favors flexibility and higher throughput; MST screens KD trends with minimal sample; ITC delivers thermodynamics when sample permits—combine as needed for mechanism.

Q: How are false positives and error rates controlled in PPI datasets?

Use matched controls, competition tests, and statistical scoring; for XL-MS, decoy-based procedures enable explicit PPI-FDR control, while AP-MS studies rely on control-aware models and replicate agreement to prioritize confident interactors.

Q: Can membrane or low-solubility complexes be profiled?

Yes—combine gentle solubilization or native capture with proximity labeling or XL-MS to preserve interactions that are lost during extraction, then verify key pairs with targeted biophysical readouts.

Q: Do I need to overexpress the bait, or can I work endogenously?

Both are used: epitope-tagged expression enables controlled enrichment, while endogenous tagging or PL in native cells can mitigate artifacts and retain context; selection depends on biology, background, and available controls.

Q: What does 'orthogonal validation' look like in a PPI project?

A common pattern is discovery (AP-MS or PL) → interface evidence (XL-MS or HDX) → quantitative binding (SPR/BLI or MST); this layered approach reduces ambiguity and links partners, sites, and kinetics for decision-ready conclusions.

Q: How fast can proximity labeling capture events and what are caveats?

Labeling windows can be short and live-cell compatible (method-dependent), enabling snapshots of local proteomes; be mindful of enzyme choice and potential labeling biases or cytotoxic reagents during activation.

Quantitative, Structural, and Mechanistic Insights

Protein–protein interactions (PPIs) form the foundation of cellular regulation, structural assembly, and signaling dynamics. However, many PPIs are weak, transient, or condition-specific—challenging to detect with a single technique.

At Creative Proteomics, we offer a comprehensive, multi-platform solution for discovering, quantifying, and validating protein interactions across biological systems. Our integrated analytical platforms ensure high-confidence, reproducible data that supports structural biology, target validation, and mechanism studies.

We Help You:

Identify weak or transient interactors under native conditions
Distinguish real partners from background noise
Quantify interaction strength, stoichiometry, and dynamics
Localize binding regions for structural or mutational design

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What Are Protein–Protein Interactions (PPIs)?

Protein–protein interactions organize cellular processes, assemble signaling complexes, and shape pathway dynamics. Measuring PPIs helps you clarify mechanism, validate targets, and de-risk development. Because many complexes are transient, weak, or context-specific, using multiple orthogonal methods is the most reliable way to confirm biological relevance.

Scientific Questions Addressed by Protein-Protein Interaction

Low-affinity or transient partners: Capture weak or short-lived contacts using cross-linking, proximity labeling, or gentle native enrichment.
False positives from tag pull-downs: Reduce background with isotype controls, competition assays, and quantitative reference channels.
Stoichiometry and binding order: Combine quantitative AP-MS with titration curves to infer complex composition and cooperativity.
Epitope masking in conformational studies: Use residue-resolved cross-links to constrain structural models and detect conformational change.
From discovery to validation: Move from broad interactome screens to kinetics and affinity confirmation (SPR/BLI/MST) using the same samples where possible.

Advantages of Our Protein-Protein Interaction Service

High-Confidence Results — 1% FDR filtering with ranked interaction scores

All interaction data are filtered using stringent false discovery thresholds (FDR ≤ 1%) and delivered with ranked confidence scores to support downstream validation and publication.

Quantitative Comparison Across Conditions — CV-controlled differential analysis

Our workflows support condition-specific comparisons with replicate-based variation control. Interactors showing ≥2-fold change and <20% coefficient of variation are highlighted for biological interpretation.

Compatible with Challenging Samples — Low-input and complex matrices supported

Optimized protocols enable reliable detection from difficult sample types, including membrane fractions, tissue lysates, and low-yield co-immunoprecipitates.

Ready for Structural & Functional Follow-Up — Region-level summaries for design input

Binding site evidence is mapped at the peptide or domain level, providing actionable data for mutagenesis, docking, or antibody targeting.

Transparent Deliverables — Complete datasets, search parameters, and QC metrics

We provide raw and processed data, annotated spectra, interaction maps, and method parameters to support internal review, reproducibility, and cross-project integration.

Technical Services

Selection Guide Workflow Application Deliverables Case FAQ Get a Custom Proposal

Protein-Protein Interaction Technology Classification & Selection Guide

PPI Techniques by Research Goal (Discovery, Quantitation, Interface Mapping)

Your Goal	Primary Choice	Complement / Validation	Typical Outputs	Input Requirements
Discover interaction partners in native context	AP-MS / TAP / Co-IP-MS	Pull-down; Y2H for hypothesis seeding	Partner list with confidence, enrichment stats, interactome graph	Validated antibody or tag; appropriate controls
Capture transient/weak or proximal neighbors	Proximity Labeling (BioID/APEX/TurboID)	Cross-check with AP-MS	Proximal proteome, pathway/GO enrichment	Fusion constructs; suitable localization
Quantify affinity and kinetics	SPR / BLI	MST (screening); ITC (thermodynamics)	Kd, kon, koff; binding models; ΔH/ΔS	Purified proteins/ligands; buffer and surface strategy
Compare variants or conditions with limited sample	MST	SPR for kinetic resolution	Kd trends; competition curves	Minimal sample; fluorescence/readout compatibility
Map interface & apply restraints	XL-MS → Docking/MD	Site-directed mutants; competition assays; ITC	Crosslinks, ranked models, hotspot residues	Purified complex or in-cell crosslinking feasibility
Visualize where/when interactions occur	FRET (distance/dynamics) or BiFC/Split-GFP (presence/localization)	FRAP for exchange; co-localization markers	FRET efficiencies, localization images, recovery curves	Validated constructs; imaging capability
High-throughput initial screening	Y2H / Protein Arrays	Move positives to AP-MS or SPR/MST	Positive hits/parallel signals	Yeast or array acceptance; follow-up path defined
Stabilize transient complexes for analysis	Covalent Crosslinking	XL-MS; SDS-PAGE/Western	Stabilized complexes; crosslink patterns	Crosslinker selection; quench/cleanup optimization
Exploratory capture in vitro	Pull-Down(± crosslinking)	MS identification → SPR/MST	Eluates and candidate proteins	Purified bait; appropriate matrix and controls

Guiding principle: Discovery → Quantitation → Mechanism. Use AP-MS/BioID to find, SPR/MST to measure, then XL-MS + Docking/MD and mutants to explain.

Comparison of Protein–Protein Interaction Techniques and Assays

Discovery / Imaging / Screening

Method	Best Use	Primary Outputs	Advantages	Limitations / Pitfalls	Common Complements
AP-MS / TAP	Stable complexes; native discovery	Partner list, enrichment stats, interactome	Physiological relevance; TAP boosts specificity	Tag/antibody dependence; lysis/wash bias	Reverse IP; SPR/MST; BioID cross-check
Co-IP-MS	Known bait interactors	Identified partners	Simple, accessible	Antibody cross-reactivity; nonspecific binding	Western; SPR/MST
Proximity Labeling (BioID/APEX/TurboID)	Transient/nearby partners in cells	Proximal proteome; GO/pathway enrichment	Captures weak/dynamic neighbors	Localization bias; promiscuous labeling	AP-MS cross-validation; SPR/MST
Y2H	High-throughput binary screen	Positive clones	Scalable, economical	Nuclear context; false positives/negatives	AP-MS or SPR/MST follow-up
Protein Arrays	Parallel interaction scans	Array signals, ranked candidates	High throughput	Missing native PTMs/conformation	SPR/MST; Pull-down
FRET (incl. microscopy)	Nanometer distance; real-time dynamics	FRET efficiency/time courses	High spatiotemporal resolution	Labeling complexity; photobleaching	FRAP; co-localization controls
BiFC / Split-GFP	Yes/no + localization in cells	Reconstituted fluorescence; images	Intuitive visualization	BiFC often irreversible; limited dynamics	FRET/FRAP; mutant controls
FRAP	Mobility/exchange of complexes	Recovery curves; mobile fraction	Simple dynamic readout	Limited spatial detail	Combine with FRET/co-localization

Quantitation / Structure / In-Vitro

Method	Best Use	Primary Outputs	Advantages	Limitations	Common Complements
SPR / BLI	Affinity and kinetics	Kd, kon, koff; model fits	Real-time; mechanism insight	Surface immobilization artifacts	MST; ITC; reverse orientations
MST	Rapid Kd trends; variant ranking	Kd; competition curves	Minimal sample; buffer-tolerant	Fluorescent labeling/temperature constraints	SPR for kinetics
ITC	Thermodynamics & stoichiometry	Kd, ΔH/ΔS, n	Label-free; direct energetics	Higher sample requirement	SPR/MST
Pull-Down	Exploratory capture	Input/eluate IDs	Simple; flexible	Nonspecific adsorption	MS ID; SPR verification
Covalent Crosslinking	Stabilize transient complexes	Crosslink patterns/bands	Locks short-lived states	Requires optimization	XL-MS; SDS-PAGE/Western
XL-MS	Interface mapping with restraints	Crosslinked peptides; distance pairs	In-situ spatial constraints	Complex peptide ID	Docking/MD; site-directed mutants
Docking + MD	Complex models; mechanism hypotheses	Ranked poses, contact maps, hotspots	Mechanistic insight; guides mutants	Needs experimental restraints	XL-MS constraints; SPR/MST
PCA (Fragment Complementation)	Functional readout of complementation	Reconstituted activity/signal	Quantifies effect of binding	May stabilize complexes artificially	SPR/MST; FRET
Split-GFP	Presence/localization (visual)	GFP reconstitution; images	Straightforward	Limited kinetic insight	FRET; FRAP

Quick Decision Guide for Selecting PPI Strategies

What's your primary question?
Find partners → AP-MS/TAP or Co-IP-MS (+ Proximity Labeling for transient)
Measure strength/mechanism → SPR/BLI (screen with MST; add ITC for thermodynamics)
Map the interface → XL-MS → Docking/MD → mutants + SPR/MST
See where/when → FRET / BiFC / Split-GFP (+ FRAP)
Screen widely → Y2H / Protein Arrays → validate positives
What inputs are feasible now?
Purified proteins → SPR/BLI/MST/ITC, XL-MS, Pull-down
Cells/tissues only → AP-MS/TAP or Proximity Labeling; add purification later for validation
What behavior do you expect?
Stable complexes → prioritize AP-MS/TAP
Transient/weak → Proximity Labeling; consider covalent crosslinking + XL-MS

Recommended Method Combinations for Different PPI Study Scenarios

Unknown interactome (native context) → AP-MS/TAP → prioritize overlaps/controls → SPR/MST for top candidates → optional FRET/BiFC for localization
Suspected weak/transient interaction → Proximity Labeling → MST screen → SPR/BLI kinetics → optional ITC
Interface & mechanism → Covalent Crosslinking + XL-MS → Docking/MD with restraints → hotspot mutants → SPR/MST and/or FRET
High-throughput to precision → Y2H / Protein Arrays → Pull-down + MS on positives → SPR/MST for quantitative ranking
Reporter-based functional readout → PCA to compare WT/mutant/compound → SPR for kinetic clarity; FRAP for exchange in cells
Challenging targets (e.g., membrane, low abundance) → Proximity Labeling → gentle Co-IP-MS → later BLI/SPR with suitable reconstitution

Protein-Protein Interaction Analysis Workflow at Creative Proteomics

Workflow for Protein-Protein Interaction Service

Applications of Protein-Protein Interaction Service

Mechanism of action mapping

Clarify pathway wiring and identify interaction switches under defined stimuli.

Interface delineation for engineering

Localize contact regions to guide mutagenesis, construct design, and stability tuning.

Antibody/biologic epitope strategy

Resolve epitope–paratope footprints and assess competition with native partners.

Small-molecule PPI modulator assessment

Evaluate inhibitor or stabilizer effects on complex formation and selectivity.

PROTAC / molecular glue optimization

Characterize ternary complex formation and cooperativity to inform linker and warhead choices.

State-dependent binding

Test how PTMs, cofactors, or ligands gate interactions across conditions.

Membrane and receptor assemblies

Profile low-solubility signaling complexes in native or stimulated contexts.

Target deconvolution for phenotypic hits

Identify direct binders and proximal partners of active compounds to explain cellular effects.

Deliverables: What You Get from Our Protein-Protein Interaction Service

Ranked interactor tables (FDR-controlled)
Condition comparisons with replicate QC
Control/background assessment summary
Interface highlights and contact maps (if applicable)
Modeling-ready restraints and region summaries
Kinetics/affinity reports with fit models (if applicable)
Figure-ready visuals: networks, volcano, heatmaps, curves

Pathway/GO enrichment overview
QC report: thresholds, contamination checks, instrument notes
Method provenance: parameters and pipeline versions
Raw MS data and processed tables
Biophysical data exports (sensorgrams/traces)
Optional targeted verification (PRM/SRM)
Optional orthogonal confirmation plan

Volcano plot of AP-MS interactors (log2FC vs −log10 p) with FDR curve and a small replicate-QC scatter showing high correlation.

AP-MS Volcano with Replicate QC inset

Three-panel kinetics figure: overlaid sensorgrams, global fit/steady-state curve with KD, and residuals centered near zero.

SPR/BLI Kinetics Panel

XL-MS contact matrix plus cartoon structure with cross-links and a histogram showing most link distances within the allowed range.

XL-MS Contact Map and Distance-Restrained Model

HDX-MS heatmap of differential deuterium uptake over time and paired butterfly plots showing increased and decreased uptake.

HDX-MS Differential Uptake

Case Study

Case 1

Case 2

Case 1: In-cell Partner Discovery with Proximity Labeling (TurboID)

Title: TurboID-based proximity labeling reveals that UBR7 is a regulator of N NLR immune receptor–mediated immunity

Journal: Nature Communications

DOI: 10.1038/s41467-019-11202-z

Research Objective: Detect native, weak/short-lived neighbors of an immune receptor in living cells.

How Proximity Labeling Was Used: TurboID tag on the bait; biotin labeling in vivo; streptavidin enrichment → LC–MS/MS.

Key Findings: TurboID expanded the proximal proteome and highlighted UBR7 as a regulator, revealing interactors difficult to capture by classic pull-downs.

Why It Matters:Shows a practical route to shortlist context-dependent interactors for orthogonal validation.

Case Study 2 — Cross-Linking MS with Explicit Error Control

Title: Reliable identification of protein–protein interactions by crosslinking mass spectrometry

Journal: Nature Communications

DOI: 10.1038/s41467-021-23666-z

Research Objective: Build a defensible PPI network with explicit false-discovery control and site-level restraints.

How XL-MS Was Used: Large-scale XL-MS on controlled lysate; decoy strategy to report PPIs at 1% PPI-FDR; structural localization of links.

Key Findings: Reported a network of hundreds of PPIs at 1% PPI-FDR, including placement of an uncharacterized protein near RNAP's exit tunnel.

Why It Matters: Delivers residue-informed, error-controlled interaction calls—ideal for modeling and targeted validation.

You May Want to Know

How do I choose a method to detect transient or weak PPIs?

Use proximity labeling in living cells to capture neighbors within a limited radius, then corroborate priority hits with orthogonal assays; PL complements enrichment-based methods by recovering short-lived or compartment-restricted contacts.

What's the practical difference between AP-MS and proximity labeling?

AP-MS enriches stable complexes under near-native conditions and excels at condition-dependent interactomes, while proximity labeling biotin-tags nearby proteins in cells to reveal weak/transient neighbors; together they provide overlapping but distinct views of an interactome.

When should I use XL-MS and what does it add?

XL-MS supplies residue-pair distance restraints that localize binding interfaces and support integrative modeling; large-scale studies show PPIs can be reported at controlled PPI-FDR (for example, 1%) to improve confidence in interaction calls.

SPR, BLI, MST, or ITC: which for affinity and kinetics?

SPR provides sensitive, real-time kinetics (kon/koff, KD) and is strong for small-molecule or fragment binding; BLI favors flexibility and higher throughput; MST screens KD trends with minimal sample; ITC delivers thermodynamics when sample permits—combine as needed for mechanism.

How are false positives and error rates controlled in PPI datasets?

Use matched controls, competition tests, and statistical scoring; for XL-MS, decoy-based procedures enable explicit PPI-FDR control, while AP-MS studies rely on control-aware models and replicate agreement to prioritize confident interactors.

Can membrane or low-solubility complexes be profiled?

Yes—combine gentle solubilization or native capture with proximity labeling or XL-MS to preserve interactions that are lost during extraction, then verify key pairs with targeted biophysical readouts.

Do I need to overexpress the bait, or can I work endogenously?

Both are used: epitope-tagged expression enables controlled enrichment, while endogenous tagging or PL in native cells can mitigate artifacts and retain context; selection depends on biology, background, and available controls.

What does "orthogonal validation" look like in a PPI project?

A common pattern is discovery (AP-MS or PL) → interface evidence (XL-MS or HDX) → quantitative binding (SPR/BLI or MST); this layered approach reduces ambiguity and links partners, sites, and kinetics for decision-ready conclusions.

How fast can proximity labeling capture events and what are caveats?

Labeling windows can be short and live-cell compatible (method-dependent), enabling snapshots of local proteomes; be mindful of enzyme choice and potential labeling biases or cytotoxic reagents during activation.