openfold3 Model by Openfold

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
Pull and Run the NIM

Bash

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

Step 3
Start the OpenFold3 NIM

Export the NGC_API_KEY environment variable.

Bash

export NGC_API_KEY=<your personal NGC key>

The NIM container automatically downloads any required models. For OpenFold3, download should take about 5 minutes. 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
chmod -R 777 $LOCAL_NIM_CACHE

Run the NIM container with the following commands.

Bash

docker run -it \
    --runtime=nvidia \
    --shm-size=16gb \
    -p 8000:8000 \
    -e NGC_API_KEY \
    -v "$LOCAL_NIM_CACHE":/opt/nim/.cache \
    nvcr.io/nim/openfold/openfold3:latest

This will by default run on all available GPUs. Below is an example of running the NIM specifically on device 0:

Bash

docker run -it \
    --runtime=nvidia \
    --gpus='"device=0"' \
    --shm-size=16gb \
    -p 8000:8000 \
    -e NGC_API_KEY \
    -v "$LOCAL_NIM_CACHE":/opt/nim/.cache \
    nvcr.io/nim/openfold/openfold3:latest

Query the NIM

The following python script can be saved to a file named openfold3.py and can then be run using python openfold3.py. This will post a request to the locally-running NIM and save the response to output.json.

Python

#!/usr/bin/env python3
import requests
import os
import json
from pathlib import Path

output_file = "output.json"
url = "http://localhost:8000/biology/openfold/openfold3/predict"

# Define protein sequence
protein_sequence = "MGREEPLNHVEAERQRREKLNQRFYALRAVVPNVSKMDKASLLGDAIAYINELKSKVVKTESEKLQIKNQLEEVKLELAGRLEHHHHHH"

# Define MSA alignment in CSV format
msa_alignment_csv = "key,sequence\n-1,MGREEPLNHVEAERQRREKLNQRFYALRAVVPNVSKMDKASLLGDAIAYINELKSKVVKTESEKLQIKNQLEEVKLELAGRLEHHHHHH"

# Define DNA sequences (complementary pair)
dna_sequence_b = "AGGAACACGTGACCC"
dna_sequence_c = "TGGGTCACGTGTTCC"

# Build request data
data = {
    "request_id": "5GNJ",
    "inputs": [
        {
            "input_id": "5GNJ",
            "molecules": [
                {
                    "type": "protein",
                    "id": "A",
                    "sequence": protein_sequence,
                    "msa": {
                        "main_db": {
                            "csv": {
                                "alignment": msa_alignment_csv,
                                "format": "csv",
                            }
                        }
                    }
                },
                {
                    "type": "dna",
                    "id": "B",
                    "sequence": dna_sequence_b
                },
                {
                    "type": "dna",
                    "id": "C",
                    "sequence": dna_sequence_c
                }
            ],
            "output_format": "pdb"
        }
    ]
}

r = requests.post(url=url, json=data)
print(r, "Saving to output.json:\n", r.text[:200], "...")
Path(output_file).write_text(r.text)

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

openfold3

Step 1Generate API Key

Step 2Pull and Run the NIM

Step 3Start the OpenFold3 NIM

Step 1
Generate API Key

Step 2
Pull and Run the NIM

Step 3
Start the OpenFold3 NIM