# Protein-protein interaction analysis: scoring AP-MS and BioID, building networks, modeling structures
URL: https://omicsquest.com/insights/protein-protein-interaction-analysis-guide/

Analyzing protein-protein interaction data: SAINT and CRAPome scoring for AP-MS and BioID, quantitative interactomics, networks, AlphaFold-Multimer.

Key takeaways:
- Most proteins in a pull-down are background. **Score interactions against matched controls** (SAINTexpress) and contaminant repositories (CRAPome).
- Proximity labeling (BioID, TurboID) maps neighborhoods of roughly 10 nm, not only direct binders.
- Database networks (STRING, BioGRID) add context but carry literature bias toward well-studied proteins.
- AlphaFold-Multimer predictions are hypotheses; report interface confidence (ipTM, pDockQ).

Protein-protein interactions explain function: which complex a protein joins, which pathway it signals through, which partner a mutation disrupts. Experimental interaction data is noisy by nature. Every purification pulls down sticky, abundant proteins along with real partners. The analysis is mostly about separating the two.

## Experimental approaches and what they measure

| Method | What it captures | Main analysis challenge |
|---|---|---|
| AP-MS / co-IP-MS | Stable complexes with the bait | Nonspecific binders |
| BioID / TurboID / APEX | Proteins within ~10 nm over time | Neighbors vs direct binders |
| Cross-linking MS | Residue-level contacts | Search complexity, FDR |
| Yeast two-hybrid | Binary interactions | False positives and negatives |
| Co-fractionation MS | Complexes across fractions | Profile correlation scoring |

## Step 1: Quantification

Search raw files with MaxQuant, FragPipe or DIA-NN, with protein-level FDR control. Choose quantification (spectral counts, MS1 intensity, or TMT for multiplexed comparisons) to fit the scoring method, and keep all controls in the same search.

## Step 2: Score against controls

- **SAINTexpress** models each prey's abundance in bait purifications against negative controls (empty vector, GFP-only bait, IgG) and returns a probability and Bayesian FDR.
- **Fold change over control** adds an effect-size view.
- **CRAPome** frequencies show how often a protein appears in unrelated pull-downs. Frequent fliers such as ribosomal proteins, chaperones and keratins need very strong evidence.

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**What our experts avoid:** calling every protein enriched over a single control an interactor. We require replicate controls, probabilistic scoring and background frequencies. A bait-specific network of 30 confident interactors is more useful than 300 enriched proteins.
</div>

## Step 3: Quantitative interactomics

When the question is how interactions change with mutation, drug treatment or stimulation, use quantitative designs (label-free with replicates, or TMT) and test differential interaction with limma-style models. Normalize carefully to bait abundance, because a bait that is expressed less pulls down less of everything.

## Step 4: Networks

- **Combine** experimental interactors with curated databases (STRING, BioGRID, IntAct) for context.
- **Detect complexes and modules** with clustering (MCL, ClusterONE) and test enrichment against known complexes (CORUM).
- **Network propagation** prioritizes genes from screens or GWAS by their proximity to disease modules.
- Remember **literature bias**: well-studied proteins have more known interactions, which inflates their apparent centrality.

## Step 5: Structural modeling

AlphaFold-Multimer and AlphaFold 3 can model candidate binary interactions and complexes. We use interface confidence (ipTM, pDockQ) to rank candidates, map disease variants and PTMs onto predicted interfaces, and propose mutations for validation. Low-confidence interfaces stay hypotheses.

## How OmicsQuest can help

Our [PPI analysis service](/services/protein-protein-interaction-analysis/) scores AP-MS, proximity labeling and Y2H data, builds Cytoscape-ready networks and models complexes. For global and phospho-proteomics, see [proteomics analysis](/services/proteomics-analysis/).

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Source: https://omicsquest.com/insights/protein-protein-interaction-analysis-guide/ | OmicsQuest, contact@omicsquest.com