deepmind/alphafold2-multimer

Pull and run deepmind/alphafold2-multimer using Docker (this will download the full model and run it in your local environment)

Get API KeyCopy Code
$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>

Start NIM

1. Export NGC_CLI_API_KEY variable.

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export NGC_CLI_API_KEY=<your personal NGC key>

2. The NIM container automatically downloads any required models. To save time and bandwidth it is recommended to provide a local cache directory. This way the NIM will be able to reuse any already downloaded models. Execute the following command to setup the cache directory:

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export LOCAL_NIM_CACHE=~/.cache/nim
mkdir -p $LOCAL_NIM_CACHE

Note that you may need to run (sudo) chmod -R 777 $LOCAL_NIM_CACHE after the AlphaFold2 model is downloaded to avoid permission issues.

3. Run the NIM container with the following commands:

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

This command will start the NIM container and expose port 8000 for the user to interact with the NIM.

4. Open a new terminal, leaving the terminal open with the just launched service. 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.

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

Python Client Example

1. Save following Python example to a file named nim_client.py.

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import requests
import json

url = "http://localhost:8000/protein-structure/alphafold2/multimer/predict-structure-from-sequences"  # Replace with the actual URL
sequences = ["MNVIDIAIAMAI", "NESKHCAWVMIPTFRQYDGL"]  # Replace with the actual sequences.

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

data = {
    "sequences": sequences,
    "databases": ["small_bfd"],
    "e_value": 0.000001,
    "algorithm": "jackhmmer",
    "num_predictions_per_model" : 1,
    "relax_prediction": False,
}

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

# Check if the request was successful
if response.ok:
    with open("output.pdb", "w") as ofi:
        ofi.write(json.dumps(response.json()))
    print("Request succeeded:", response.json())
else:
    print("Request failed:", response.status_code, response.text)

2. Execute the example.

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python nim_client.py

3. The resulting PDB structure will be returned and written to output.pdb.

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cat output.pdb

Shell Client Example

1. Save the following Shell example to a file named nim_client.sh.

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#!/usr/bin/env bash
set -e

URL=http://localhost:8000/protein-structure/alphafold2/multimer/predict-structure-from-sequences

request='{
 "sequences": ["MNVIDIAIAMAI", "NESKHCAWVMIPTFRQYDGL"]
}'
curl -H 'Content-Type: application/json' \
     -d "$request" "$URL"

2. Execute the example.

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chmod +x nim_client.sh

./nim_client.sh

3. Results will be printed on the terminal in JSON format. You will be able to see the PDB formatted output; you can also use curl to save the output directly to file.