# STRING Protein-Protein Interactions Scraper (`ninhothedev/string-interactions-scraper`) Actor

$0.5/1K 🔥 STRING PPI Network! Map protein-protein interactions with confidence scores & partners. No key. JSON, CSV, Excel or API in seconds. Build interactomes for systems-biology studies ⚡

- **URL**: https://apify.com/ninhothedev/string-interactions-scraper.md
- **Developed by:** [ninhothedev](https://apify.com/ninhothedev) (community)
- **Categories:** Developer tools, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.50 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## STRING Protein-Protein Interactions Scraper

Scrape **protein-protein interaction (PPI) networks** from [STRING-DB](https://string-db.org)
— combined confidence scores plus all seven evidence channels, top interaction
partners per protein, and name-to-STRING-id resolution. **No API key required.**

STRING is the reference database of known and predicted protein-protein
interactions, integrating experimental data, curated pathway databases, genomic
context and automated text-mining into a single **confidence-scored** network.
This actor turns that into clean, tabular JSON you can drop into a spreadsheet,
a graph tool, or a data pipeline.

Runs at roughly **~$0.50 per 1,000 records** on the Apify platform.

***

### What it does

Three modes:

| Mode | What you get |
|------|--------------|
| **network** | Interactions **among a given set** of proteins (the edges inside your list). |
| **partners** | The **top interaction partners** of each input protein (network expansion). |
| **resolve** | Map protein **names/gene symbols to STRING ids** + annotation. |

#### Evidence channels

Every interaction carries a `combined_score` and the seven STRING evidence
channels, so you know *why* two proteins are linked:

| Field | Channel | Meaning |
|-------|---------|---------|
| `neighborhood_score` | n | Conserved genomic neighborhood |
| `fusion_score` | f | Gene fusion events |
| `phylogenetic_score` | p | Phylogenetic co-occurrence |
| `coexpression_score` | a | Co-expression |
| `experimental_score` | e | Experimental / biochemical data |
| `database_score` | d | Curated database (pathways, complexes) |
| `textmining_score` | t | Automated literature text-mining |

The actor also computes `strongest_channel` (the channel with the highest
sub-score) so you can filter for, say, experimentally-backed edges at a glance.

#### Score scale

STRING's JSON API returns confidence as **floats in the range `0-1`**
(e.g. `0.999`). Some STRING outputs multiply these by 1000. Each record includes
a `score_scale` field reporting the **observed** range (`"0-1"` here), and the
scraper detects and labels `0-1000` automatically if the API ever returns
integer-scaled values.

***

### Input

| Field | Type | Default | Description |
|-------|------|---------|-------------|
| `mode` | select | `network` | `network`, `partners`, or `resolve`. |
| `identifiers` | array | `["TP53","EGFR","MDM2","BRCA1"]` | Protein names / gene symbols / STRING ids. |
| `species` | string | `9606` | NCBI taxonomy id (9606 = human, 10090 = mouse, 7227 = fly, 4932 = yeast). |
| `partnerLimit` | integer | `20` | Max partners per protein (partners mode only). |
| `maxItems` | integer | `500` | Max records to output (hard cap 5000). |

#### Example

```json
{
  "mode": "network",
  "identifiers": ["TP53", "EGFR", "MDM2", "BRCA1"],
  "species": "9606",
  "maxItems": 500
}
```

***

### Output

**Interaction** record (`network` / `partners`):

```json
{
  "type": "interaction",
  "protein_a": "MDM2",
  "protein_b": "TP53",
  "string_id_a": "9606.ENSP00000258149",
  "string_id_b": "9606.ENSP00000269305",
  "tax_id": "9606",
  "combined_score": 0.999,
  "neighborhood_score": 0.0,
  "fusion_score": 0.0,
  "phylogenetic_score": 0.0,
  "coexpression_score": 0.116,
  "experimental_score": 0.999,
  "database_score": 0.9,
  "textmining_score": 0.999,
  "score_scale": "0-1",
  "strongest_channel": "experimental",
  "source": "string-db",
  "scraped_at": "2026-08-11T00:00:00Z"
}
```

**Resolved** record (`resolve`):

```json
{
  "type": "resolved",
  "query": "TP53",
  "string_id": "9606.ENSP00000269305",
  "preferred_name": "TP53",
  "tax_id": 9606,
  "taxon_name": "Homo sapiens",
  "annotation": "Cellular tumor antigen p53; Acts as a tumor suppressor ...",
  "source": "string-db",
  "scraped_at": "2026-08-11T00:00:00Z"
}
```

All fields are nullable.

***

### Use cases

- **Network biology** — build and export PPI graphs for a gene set.
- **Drug target discovery** — find the partners of a target and rank edges by
  experimental evidence.
- **Systems biology** — assemble multi-protein interaction maps across species.
- **Gene set analysis** — resolve identifiers and pull interactions for a panel
  of genes from an experiment.

***

### How this differs from our other biology scrapers

This account publishes several life-science data actors. Pick the one that
matches the *entity* you care about:

- **This actor (STRING-DB)** → protein-protein **interaction networks**: which
  proteins bind/associate with which, with **confidence scores and evidence
  channels**. It is about **edges between proteins**.
- **[Reactome Pathways Scraper](https://apify.com/ninhothedev/reactome-pathways-scraper)**
  → curated biological **pathways and reactions** (ordered mechanistic steps),
  not scored interaction networks. Use Reactome when you want *pathway
  membership and reaction hierarchy*; use STRING when you want the *interaction
  graph and its confidence*.
- **[UniProt Scraper](https://apify.com/ninhothedev/uniprot-scraper)**
  → deep annotation of **single proteins** (sequence, function, domains, GO,
  cross-refs). UniProt describes *one protein*; STRING describes *relationships
  between proteins*.
- **[AlphaFold Structures Scraper](https://apify.com/ninhothedev/alphafold-structures-scraper)**
  → predicted 3D **structures** of individual proteins.
- **[ChEMBL Scraper](https://apify.com/ninhothedev/chembl-scraper)**
  → **bioactivity** of small molecules against targets.

In short: UniProt = the protein, AlphaFold = its shape, ChEMBL = drugs hitting
it, Reactome = the pathway it sits in, **STRING = who it talks to**.

***

### Data source, licensing & etiquette

- Data © [STRING](https://string-db.org) consortium, distributed under
  **[Creative Commons Attribution 4.0 (CC BY 4.0)](https://creativecommons.org/licenses/by/4.0/)**.
  Please cite STRING (Szklarczyk et al., *Nucleic Acids Research*) when you use
  the data.
- The actor sends a `caller_identity` parameter with every request, as STRING
  requests. No API key is needed.
- Please keep request volumes reasonable and respect STRING's
  [access guidelines](https://string-db.org/cgi/help).

This is an unofficial scraper and is not affiliated with or endorsed by the
STRING consortium.

# Actor input Schema

## `mode` (type: `string`):

What to fetch. 'network' returns interactions among the given protein set. 'partners' returns the top interaction partners of each protein. 'resolve' maps protein names/symbols to STRING identifiers.

## `identifiers` (type: `array`):

Protein names, gene symbols or STRING ids to query (e.g. TP53, EGFR). Multiple identifiers are joined with the STRING carriage-return separator automatically.

## `species` (type: `string`):

NCBI taxonomy id of the organism. Default '9606' = Homo sapiens. Examples: 10090 (mouse), 7227 (fly), 4932 (yeast).

## `partnerLimit` (type: `integer`):

Maximum number of top interaction partners to return per input protein. Only used in 'partners' mode.

## `maxItems` (type: `integer`):

Maximum number of records to push to the dataset across the whole run. Hard-capped at 5000.

## Actor input object example

```json
{
  "mode": "network",
  "identifiers": [
    "TP53",
    "EGFR",
    "MDM2",
    "BRCA1"
  ],
  "species": "9606",
  "partnerLimit": 20,
  "maxItems": 500
}
```

# Actor output Schema

## `results` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "identifiers": [
        "TP53",
        "EGFR",
        "MDM2",
        "BRCA1"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("ninhothedev/string-interactions-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "identifiers": [
        "TP53",
        "EGFR",
        "MDM2",
        "BRCA1",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("ninhothedev/string-interactions-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "identifiers": [
    "TP53",
    "EGFR",
    "MDM2",
    "BRCA1"
  ]
}' |
apify call ninhothedev/string-interactions-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,ninhothedev/string-interactions-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Zdo5JJfCC6lrejDew/builds/AyEMsP8TjuF2DS1ln/openapi.json