> This page is for AlphaFold2-Multimer, version 1.0.0 (Latest) (default).
> For other versions, use one of these documentation indexes:
> - 1.0.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/alphafold2-multimer/1.0.0/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server.

# Quickstart Guide

<Note>
This guide assumes you have installed and set up all [software prerequisites](/nim/bionemo/alphafold2-multimer/prerequisites), including Docker authentication, NGC CLI installation, NGC registry access, and creating a location for the NIM model cache.
</Note>

1. Pull the NIM container with the following command.

```bash
docker pull nvcr.io/nim/deepmind/alphafold2-multimer:1.0.0
```


2. Run the NIM container with the following command.

```bash
export LOCAL_NIM_CACHE=~/.cache/nim
export NGC_CLI_API_KEY=<Your NGC CLI API Key>
```

```bash
docker run -it --rm --name alphafold2-multimer --runtime=nvidia \
    -e NGC_CLI_API_KEY \
    -v $LOCAL_NIM_CACHE:/opt/nim/.cache \
    -p 8000:8000 \
    nvcr.io/nim/deepmind/alphafold2-multimer:1.0.0
```


This command starts the NIM container and exposes port 8000 so you can interact with the NIM. It pulls the model to the cache at `$LOCAL_NIM_CACHE` on the local filesystem.

<Note>
Downloading the AlphaFold2 model can take a very long time, up to 4 to 10 hours on a connection of 100 Mbps or faster. If the `docker run` command fails due to file permission errors after downloading the AlphaFold2 model, run the following command: `sudo chmod -R 777 $LOCAL_NIM_CACHE`. Afterward, rerun the NIM using the `docker run` command.
</Note>

3. Open a new terminal, leaving the current terminal open with the launched service.

4. In the new terminal, wait until the health check endpoint returns `{"status":"ready"}` before proceeding. This can take a couple of minutes. You can use the following command to query the health check.

```bash
curl -X 'GET' \
    'http://localhost:8000/v1/health/ready' \
    -H 'accept: application/json'
```

If you would rather check the NIM's status with Python, you can use the requests module (after `pip install requests`):

```python
import requests

url = "http://localhost:8000/v1/health/ready"  # Replace with the actual URL

headers = {
    "content-type": "application/json"
}
try:
    response = requests.get(url, headers=headers)

    # Check if the request was successful
    if response.ok:
        print("Request succeeded:", response.json())
    else:
        print("Request failed:", response.status_code, response.text)
except Exception as E:
    print("Request failed:", E)
```

5. Run inference to get a predicted protein structure for an amino acid sequence using the following command.

```bash
curl -X 'POST' \
    'http://localhost:8000/protein-structure/alphafold2/multimer/predict-structure-from-sequences' \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{"sequences": ["MNVIDIAIAMAI", "IAMNVIDIAAI"]}' > output.json
```

In Python:

```python
import requests
import json

url = "http://localhost:8000/protein-structure/alphafold2/multimer/predict-structure-from-sequences"  # Replace with the actual URL.
sequences = ["MNVIDIAIAMAI", "IAMNVIDIAAI"]  # Replace with the actual sequences you want to perform structure prediction on.

headers = {
    "content-type": "application/json"
}

data = {
    "sequences": sequences,
    "databases": ["uniref90", "small_bfd"]
}

response = requests.post(url, headers=headers, data=json.dumps(data))

# Check if the request was successful
if response.ok:
    print("Request succeeded:", response.json())
else:
    print("Request failed:", response.status_code, response.text)
```

6. View the outputs. You can use `cat` to print the outputs to the command line with the following command.

```bash
cat output.json
```

Install [`jq`](https://jqlang.github.io/jq/) to format the JSON. You can use `jq` to visualize the output from the file (you must install jq; on Linux this can be done using `apt-get install jq`):

```bash
jq . output.json
```

or you can pipe the output directly to `jq` as in the following command:

```bash
curl -X 'POST' \
    'http://localhost:8000/protein-structure/alphafold2/multimer/predict-structure-from-sequences' \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{"sequences": ["MNVIDIAIAMAI", "IAMNVIDIAAI"]}' | jq
```