> This page is for AlphaFold2, version 1.0.0.
> For other versions, use one of these documentation indexes:
> - 2.1.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/alphafold2/2.1.0/llms.txt
> - 2.0.0: https://docs.nvidia.com/nim/bionemo/alphafold2/2.0.0/llms.txt
> - 1.2.0: https://docs.nvidia.com/nim/bionemo/alphafold2/1.2.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/alphafold2/1.0.0/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 page assumes you have installed and set up [Prerequisite Software](/nim/bionemo/alphafold2/1.0.0/prerequisites) (Docker, NGC CLI, NGC registry access).
</Note>

1. Pull the NIM container with the following command.

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


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


This command will start the NIM container and expose port 8000 for the user to interact with the NIM. It will pull the model to the cache at `$LOCAL_NIM_CACHE` on the local filesystem. **Note**: this download can take a very long time (4-10 hours on a 100+Mbps internet connection).

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

4. In the new terminal, wait until the health check end point returns `{"status":"ready"}` before proceeding. This may 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 via python, you can use the requests module (once installed via `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/predict-structure-from-sequence' \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{"sequence": "MNVIDIAIAMAI"}' > output.json
```

In python:

```python
import requests
import json

url = "http://localhost:8000/protein-structure/alphafold2/predict-structure-from-sequence"  # Replace with the actual URL
sequence = "MNVIDIAIAMAI"  # Replace with the actual sequence value

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

data = {
    "sequence": sequence,
    "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 the cat tool print the outputs to the command line as with the following command.

```bash
cat output.json
```

However, we recommend installing [`jq`](https://jqlang.github.io/jq/), a command line tool that can format JSON for improved readability. You can use `jq` to visualize the output from the file (note, 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/predict-structure-from-sequence' \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{"sequences": "MNVIDIAIAMAI"}' | jq
```