alphafold2 Model by DeepMind

Follow the steps below to download and run the NVIDIA NIM inference microservice for this model on your infrastructure of choice.

Step 1
Generate API Key

Step 2
Start NIM

Export NGC_API_KEY variable.

Bash

export NGC_API_KEY=<your personal NGC key>

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:

Bash

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

Run the NIM container with the following commands.

Bash

$ docker login nvcr.io
Username: $oauthtoken
Password: <PASTE_API_KEY_HERE>

Bash

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

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

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.

Bash

curl http://localhost:8000/v1/health/ready

Step 3
Test the NIM

Python client example

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

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": ["small_bfd"],
    "e_value": 0.000001,
    "algorithm": "mmseqs2",
    "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)

Execute the example.

Bash

python nim_client.py

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

Bash

cat output.pdb

Shell client example

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

Bash

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

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

request='{
 "sequence": "MNVIDIAIAMAI"
}'
curl -H 'Content-Type: application/json' \
     -d "$request" "$URL"

Execute the example.

Bash

chmod +x nim_client.sh

./nim_client.sh

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.

For more details on getting started with this NIM, visit the NVIDIA NIM Docs.

alphafold2

Step 1Generate API Key

Step 2Start NIM

Step 3Test the NIM

Python client example

Shell client example

Step 1
Generate API Key

Step 2
Start NIM

Step 3
Test the NIM