Migrate From HHR-Based Templates to Explicit mmCIF Templates

Purposes of 2.0.0 Changes

OpenFold2 version 2.0.0 removes support for an internal search for structural templates based on HHR-format input, but maintains support for explicitly provided mmCIF template structures. The functionality to search a database of structure files will be moved to the NVIDIA MSA-NIM, and supported in the MSA-NIM in future releases. This change consolidates the management of sequence and structure databases to the MSA-NIM. This change provides several benefits for the OpenFold2 NIM:

• Simpler dependency set: No bundled PDB70 database required
• Lower startup time and disk footprint: Reduced container size and faster initialization
• Greater flexibility: Direct control over template structures without database limitations

Removed Features

The following features have been removed in version 2.0.0:

• All request fields named templates or sub-keys expecting HHSearch (HHR) data
• Environment variables and NIM cache content for PDB70 are ignored
• CLI flags or examples that mentioned use_templates=true with HHR format
• Database-driven template processing pipeline

Step-by-Step Migration

Follow the steps below to migrate the HHR-based template to explicit mmCIF templates.

1. Prepare Your Local mmCIF Template Structure

This guide assumes you already have mmCIF template files available locally. If you need to convert existing PDB files to mmCIF format, you can use tools like BioPython:

# Using BioPython to convert PDB to mmCIF format
python -c "
from Bio.PDB import PDBParser, MMCIFIO
parser = PDBParser()
structure = parser.get_structure('protein', 'input.pdb')
io = MMCIFIO()
io.set_structure(structure)
io.save('output.cif')
"

2. Update Your JSON Request Format

Replace the old JSON request format with the new format.

Old format (≤1.2.0):

{
    "sequence": "GGSKENEISHHAKEIERLQKEIERHKQ...",
    "use_templates": true,
    "templates": {
        "pdb70": {
            "hhr": {
                "format": "hhr",
                "templates": "HHR_STRING_CONTENT_HERE"
            }
        }
    }
}

New format (2.0.0 and later):

{
    "sequence": "GGSKENEISHHAKEIERLQKEIERHKQ...",
    "use_templates": true,
    "explicit_templates": [
        {
            "name": "2yz6_A",
            "format": "mmcif",
            "structure": "STRINGIFIED_MMCIF_CONTENT_HERE",
            "source": "pdb",
            "rank": -1
        }
    ]
}

3. Update Python Code Examples

Update your Python code examples as follows:

Complete Python example:

import requests
import json
# Read local mmCIF file content
with open('path/to/your/template.cif', 'r') as f:
    mmcif_content = f.read()
# Prepare request. The request body is JSON, so "structure" must be a string
# containing the mmCIF text. Set "format" to "mmcif.gz" to have the NIM
# gzip-compress the string server-side for large templates.
data = {
    "sequence": "GGSKENEISHHAKEIERLQKEIERHKQ...",
    "input_id": "example_protein_001",
    "selected_models": [1, 2],
    "alignments": {
        "uniref90": {
            "a3m": {
                "alignment": "YOUR_A3M_ALIGNMENT_HERE",
                "format": "a3m"
            }
        }
    },
    "use_templates": True,
    "explicit_templates": [
        {
            "name": "my_template",
            "format": "mmcif",
            "structure": mmcif_content,
            "source": "user_provided",
            "rank": -1
        }
    ]
}
# Submit request
url = "http://localhost:8000/biology/openfold/openfold2/predict-structure-from-msa-and-template"
headers = {"Content-Type": "application/json"}
response = requests.post(url, data=json.dumps(data), headers=headers, timeout=300)

4. Optional: Clean Cached Data

Remove old PDB70 cache data that is no longer needed:

# Remove old template database cache
rm -rf ~/.cache/nim/openfold2/pdb70
rm -rf ~/.cache/nim/openfold2/templates

Common Issues and Solutions

Performance Considerations

Ensure you consider the following improvements and impact when migrating.

Expected Improvements in 2.0.0

• Faster startup: No database loading required
• Reduced memory usage: No PDB70 database in memory
• Simplified confidence scoring: Fixed maximum confidence instead of HHR scores
• Direct processing: Single-step template feature extraction

Accuracy Impact

The following are ways 2.0.0 impacts accuracy:

• Empirical tests show less than 1% RMSD change in most cases
• pLDDT scores may vary slightly due to different confidence weighting
• Overall structure quality remains comparable

Advanced Usage Patterns

This section explores other usage patterns.

Multiple Templates

"explicit_templates": [
    {
        "name": "template_1",
        "format": "mmcif",
        "structure": "MMCIF_CONTENT_1",
        "source": "user_provided",
        "rank": -1
    },
    {
        "name": "template_2",
        "format": "mmcif",
        "structure": "MMCIF_CONTENT_2",
        "source": "user_provided",
        "rank": -1
    }
]

Programmatic Template Management

def load_template_from_file(file_path, template_name):
    """Load a local mmCIF template file for OpenFold2"""
    with open(file_path, 'r') as f:
        mmcif_content = f.read()
    return {
        "name": template_name,
        "format": "mmcif",
        "structure": mmcif_content,
        "source": "user_provided",
        "rank": -1
    }
# Usage
template = load_template_from_file("path/to/template.cif", "my_template")

Helpful Resources

• Refer to the updated Template Processing documentation for technical details
• Review Example Requests for complete working examples
• Contact NVIDIA support for migration assistance

Migration Checklist

• Identify all HHR-based template usage in your code
• Ensure you have template structures in mmCIF format locally available
• Update JSON request format to use explicit_templates
• Test with a small example to verify functionality
• Update any automated pipelines or scripts
• Clean up old PDB70 cache directories
• Validate that structure prediction quality meets your requirements