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Use the OpenFold3 NIM

OpenFold3 NIM predicts 3D structures of biomolecular complexes including proteins, DNA, RNA, and ligands. The NIM supports multiple prediction modes: protein-only structure prediction, protein-DNA/RNA complexes, protein-ligand interactions, and template-guided predictions.

Try It Out

The following example shows a basic protein structure prediction. This script predicts the structure of a small protein and saves the result.

import requests
# NIM endpoint
url = "http://localhost:8000/biology/openfold/openfold3/predict"
headers = {"Content-Type": "application/json"}
# Define protein sequence
protein_sequence = "MKTVRQERLKSIVR"
# Create minimal MSA (just the query sequence)
msa_content = f">query\n{protein_sequence}"
# Build the request
data = {
    "inputs": [{
        "input_id": "my_first_prediction",
        "molecules": [{
            "type": "protein",
            "sequence": protein_sequence,
            "msa": {
                "main": {
                    "a3m": {
                        "alignment": msa_content,
                        "format": "a3m"
                    }
                }
            }
        }],
        "output_format": "pdb"
    }]
}
# Submit prediction request
response = requests.post(url, json=data, headers=headers, timeout=300)
# Extract and save the predicted structure
if response.ok:
    result = response.json()
    structure = result['outputs'][0]['structures_with_scores'][0]['structure']
    
    # Save to file
    with open("predicted_structure.pdb", "w") as f:
        f.write(structure)
    
    print("✓ Prediction complete! Structure saved to predicted_structure.pdb")
else:
    print(f"✗ Prediction failed: {response.status_code} - {response.text}")

Save this script as predict.py and run it:

python predict.py

Try Out Other Prediction Use Cases

The following examples show how to customize the data field for different prediction scenarios. To try them, replace the data dictionary in the script above with these examples.

Protein-DNA Complex

Use this example for modeling protein-DNA interactions. For double-stranded DNA (dsDNA), both complementary strands must be entered as separate molecules.

# Replace the 'data' variable with this
data = {
    "inputs": [{
        "input_id": "protein_dna_complex",
        "molecules": [
            {
                "type": "protein",
                "id": "A",
                "sequence": "MKTVRQERLKSIVR",
                "msa": {
                    "main": {
                        "a3m": {
                            "alignment": ">query\nMKTVRQERLKSIVR",
                            "format": "a3m"
                        }
                    }
                }
            },
            {
                "type": "dna",
                "id": "B",
                "sequence": "ATCGATCG"
            },
            {
                "type": "dna",
                "id": "C",
                "sequence": "CGATCGAT"  # Complementary strand
            }
        ],
        "output_format": "pdb"
    }]
}

Protein-Ligand With SMILES

For modeling protein-ligand interactions using SMILES notation:

# Replace the 'data' variable with this
data = {
    "inputs": [{
        "input_id": "protein_ligand",
        "molecules": [
            {
                "type": "protein",
                "sequence": "MKTVRQERLKSIVR",
                "msa": {
                    "main": {
                        "a3m": {
                            "alignment": ">query\nMKTVRQERLKSIVR",
                            "format": "a3m"
                        }
                    }
                }
            },
            {
                "type": "ligand",
                "smiles": "CC(=O)OC1=CC=CC=C1C(=O)O"  # Aspirin
            }
        ],
        "output_format": "pdb"
    }]
}

Protein-Ligand With CCD Code

For modeling protein-ligand interactions using Chemical Component Dictionary codes:

# Replace the 'data' variable with this
data = {
    "inputs": [{
        "input_id": "protein_ligand_ccd",
        "molecules": [
            {
                "type": "protein",
                "sequence": "MKTVRQERLKSIVR",
                "msa": {
                    "main": {
                        "a3m": {
                            "alignment": ">query\nMKTVRQERLKSIVR",
                            "format": "a3m"
                        }
                    }
                }
            },
            {
                "type": "ligand",
                "ccd_codes": "ATP"  # Adenosine triphosphate
            }
        ],
        "output_format": "pdb"
    }]
}

Protein With Structural Templates

For template-guided protein structure prediction using experimental or predicted structures:

Structural templates can significantly improve prediction accuracy. For detailed information on template processing, selection strategies, and best practices, refer to Template Processing.

# First, read a template structure from a CIF file
with open("template.cif", "r") as f:
    template_cif_content = f.read()
# Replace the 'data' variable with this
data = {
    "inputs": [{
        "input_id": "template_guided_prediction",
        "molecules": [{
            "type": "protein",
            "sequence": "MKTVRQERLKSIVR",
            "msa": {
                "main": {
                    "a3m": {
                        "alignment": ">query\nMKTVRQERLKSIVR",
                        "format": "a3m"
                    }
                }
            },
            "structural_templates": [
                {
                    "structure": template_cif_content,
                    "format": "cif",
                    "name": "template_1"
                }
            ]
        }],
        "output_format": "pdb"
    }]
}

Multi-Chain Protein Complex With MSAs

For predicting complex biomolecular assemblies with multiple protein chains and detailed MSAs:

# Multi-chain protein sequences
protein1_sequence = "VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR"
protein2_sequence = "VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH"
# MSA for protein 1 with multiple homologs (a3m format)
protein1_msa_a3m = """>101
VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR
>UniRef100_A0A068F5F5
VLSAKDKTNIKTAWGKIGGHAAEYGAEALERMFVVYPTTKTYFPHFDVSHGSAQVKAHGKKVADALTNAVGHLDDLPGALSALSDLHAHKLRVDPVNFKLLSHCLLVTLANHIPADFTPAVHASLDKFLASVSTVLTSKYR
>UniRef100_A0A091II63
-LTQAEKAAVVAIWAKVAPQIDAIGAESLERLFFTYPQTKTYFPHFDLSHSSPQLRGHGSKVMNAIGEAVKNLDDLRGALVKLSELHAYILRVDPVNFKLLSHCILCSLAAHYPKDFTPEAHAAWDKFLSSVSSVLTEKYR
>UniRef100_UPI00162A5CD8
VLSPADKTNIKAAWDKVGGNVGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVGDALTNAVAHIDDLPGALSALSDLHAYKLRVDPVNFKLLSHCLLVTLASHLPSDFTPAVHASLDKFLASVSTVLTSKYR"""
# Paired MSA for protein 1 for modeling chain-chain interactions
protein1_paired_msa_a3m = """>101
VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVAHVDDMPNALSALSDLHAHKLRVDPVNFKLLSHCLLVTLAAHLPAEFTPAVHASLDKFLASVSTVLTSKYR
>UniRef100_UPI001EFC6C5E
-LTDEQKRLIQKSYAEIDRQSSNFAAIFYDCLFAMAPLIRPMFK-----SERPVFEYHFNELISTAATKVFQFEEIKPRLVVLGRKH-RGYGVTPAQFDVVRSALMLSIQDCLRDACNPAIEQAWSSYYDEIAKVM-----
>UniRef100_UPI0018E7FE8A
-LTEIEKEAITSSFTLINHQEQQFASFFYDCLFDLAPLIKPMFKR-----DRKLIEEHFYMIFCAAVDNIHHLDTIRSTLLELGSRH-RNYGVKVSHFPIVKSALILAIQHELKGQSNTDIENAWSNYYDELAAII-----"""
# MSA for protein 2 with multiple homologs (a3m format)
protein2_msa_a3m = """>102
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
>UniRef100_UPI0018F66524
VHLSAEEKSAVNSLWGKVNVEEHGGEALARLLVVYPWTQRFFDSFGNLSSASAILGNPKVKAHGKKVLTSFGDAVKNLDNLKGTFAKLSELHCDKLHVDPENFRLLGNVLVVVLARHFGKDFTPEVQAAWQKLVAGVASALAHKYH
>UniRef100_UPI001CFCA915
VHFTAEEKSTITSLWGKVNVEETGGEALGRLLVVYPWTQRFFDSFGNLSSPSAILGNPKVKAHGKKVLTSLGDAVKNLDNLKGAFSKLSELHCDKLHVDPENFRLLGNVLIVVLAAHFGKEFTPEVQAAWQKLVTGVASALAHKYH
>UniRef100_A0A8C6RZS6
VNFTPEEKSLVTSLWSKVNVEEAGGEALGRLLVVYPWTQRFFDSFGNLSSASAIMGNPRVKAHGKKVLTSFGEAVKNMDNLKATFSKLSELHCDKLHVDPENFKLLGNVLVVVLASHFGKEFTPEVQAAWQKLVAGVANALSHKYH"""
# Paired MSA for protein 2 for modeling chain-chain interactions
protein2_paired_msa_a3m = """>102
VHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPWTQRFFESFGDLSTPDAVMGNPKVKAHGKKVLGAFSDGLAHLDNLKGTFATLSELHCDKLHVDPENFRLLGNVLVCVLAHHFGKEFTPPVQAAYQKVVAGVANALAHKYH
>UniRef100_UPI001EFC6C5E
--LTDEQKRLIQKSYAEIDrqSSNFAAIFYDCLFAMAPLIRPMFKS-----------ERPVFEYHFNELISTAATKVFQFEEIKPRLVVLGRKH-RGYGVTPAQFDVVRSALMLSIQDCLRDACNPAIEQAWSSYYDEIAKVM-----
>UniRef100_UPI0018E7FE8A
--LTEIEKEAITSSFTLINHQEqqFASFFYDCLFDLAPLIKPMFKRDRKL-----------IEEHFYMIFCAAVDNIHHLDTIRSTLLELGSRH-RNYGVKVSHFPIVKSALILAIQHELKGQSNTDIENAWSNYYDELAAIILEG--"""
# Replace the 'data' variable with this
data = {
    "request_id": "1a3n",
    "inputs": [{
        "input_id": "1a3n",
        "molecules": [
            {
                "type": "protein",
                "id": ["A", "C"],
                "sequence": protein1_sequence,
                "msa": {
                    "uniref": {
                        "a3m": {
                            "alignment": protein1_msa_a3m,
                            "format": "a3m"
                        }
                    }
                },
                "paired_msa": {
                    "paired": {
                        "a3m": {
                            "alignment": protein1_paired_msa_a3m,
                            "format": "a3m"
                        }
                    }
                }
            },
            {
                "type": "protein",
                "id": ["B", "D"],
                "sequence": protein2_sequence,
                "msa": {
                    "uniref": {
                        "a3m": {
                            "alignment": protein2_msa_a3m,
                            "format": "a3m"
                        }
                    }
                },
                "paired_msa": {
                    "paired": {
                        "a3m": {
                            "alignment": protein2_paired_msa_a3m,
                            "format": "a3m"
                        }
                    }
                }
            }
        ],
        "diffusion_samples": 1,
        "output_format": "pdb"
    }]
}

Understand the API

This section provides detailed information about the OpenFold3 NIM API structure, requirements, and validation rules.

Endpoint

  • /biology/openfold/openfold3/predict: Predicts the 3D structure of a biomolecular complex from input sequences

Request Structure

A complete request consists of:

Request-Level Fields:

  • request_id (optional): Identifier for the entire request (max 128 characters)
  • inputs (required): List containing exactly one input specification

Input-Level Fields:

  • input_id (optional): Unique identifier for this structure prediction (max 128 characters, default: “input_id_0”)
  • molecules (required): List of molecules to predict as a complex (minimum 1)
  • diffusion_samples (optional): Number of independent structures to generate (1-5, default: 1)
  • output_format (optional): Output format - "cif" (default) or "pdb"

Molecule Specification

Each molecule in the molecules list requires:

Common Fields:

  • type (required): Must be "protein", "dna", "rna", or "ligand"
  • id (optional): Chain identifier(s) - single string or list of strings (1-4 alphanumeric characters each)

For Proteins, DNA, RNA:

  • sequence (required): Amino acid or nucleotide sequence (1-4096 characters)
    • Proteins: Standard single-letter amino acid codes
    • DNA: A, T, C, G
    • RNA: A, U, C, G

For Proteins (MSA required):

  • msa (conditional): Single-chain multiple sequence alignment (required unless paired_msa provided)
  • paired_msa (optional): Joint MSA for modeling chain-chain interactions
  • structural_templates (optional): List of structural templates in CIF format

For RNA (MSA required):

  • msa (required): Single-chain multiple sequence alignment

For Ligands (one required):

  • ccd_codes (conditional): Chemical Component Dictionary code (for example, “ATP”, “CL”)
  • smiles (conditional): SMILES string (for example, “CC(=O)OC1=CC=CC=C1C(=O)O”)

For proteins, either msa or paired_msa is required. If you want to provide an MSA with 0 hits, include an MSA containing only the query sequence.

MSA Structure

The msa and paired_msa fields use a nested dictionary structure:

"msa": {
    "database_name": {        # Arbitrary name (for example, "uniref90", "main_db")
        "format_name": {      # Must be "a3m" or "csv" (lowercase)
            "alignment": "...",   # MSA content as string
            "format": "csv"       # Must match format_name
        }
    }
}

MSA Requirements:

  • First sequence must exactly match the input protein/RNA sequence (without gaps)
  • Format names must be lowercase: "a3m" or "csv"
  • Maximum 3 MSA databases per protein
  • Gaps represented by - characters
  • A3M format: lowercase letters represent insertions relative to query

A3M Format Example:

>query
MKTVRQERLKSIVR
>hit1
MKTVRQERLKSIVR
>hit2
MKTVR-ERLKSIVR

CSV Format Example:

key,sequence
-1,MKTVRQERLKSIVR
-1,MKTVRQERLKSIVR
-1,MKTVR-ERLKSIVR

Structural Templates

For template-guided predictions, each template requires:

  • structure (required): CIF file contents as string
  • format (required): Must be "cif"
  • name (optional): Template identifier
  • chain_id (optional): Specific chain to use from multi-chain CIF (1-10 alphanumeric characters)

Prepare MSAs

For production use, generate MSAs using sequence homology search tools:

Recommended Tools:

  • ColabFold - Fast MSA generation
    • For multi-chain inputs with pairing, post-process the pair.a3m file by filtering null characters and splitting by chain
  • OpenFold3 MSA script - Includes colabfold mode with automatic chain splitting
  • HHBlits - Traditional MSA generation

Workflow:

  1. Run MSA tools for each distinct protein/RNA sequence
  2. Convert results to A3M or CSV format
  3. Ensure first sequence matches your input sequence exactly
  4. For multi-chain complexes, optionally generate paired MSAs

Response Structure

The response contains:

  • request_id: Echo of the request ID
  • outputs: List with a single output object:
    • input_id: Echo of the input ID
    • structures_with_scores: List of predicted structures (ranked, best first):
      • structure: Predicted structure in CIF or PDB format (string)
      • format: Output format ("cif" or "pdb")
      • confidence_score: Sample ranking score
      • complex_plddt_score: Average pLDDT score for the complex
      • complex_pde_score: Average PDE score for the complex
      • ptm_score: Predicted TM score for the complex
      • iptm_score: Predicted TM score for interfaces
    • runtime_metrics (optional): Performance metrics

Field Reference Tables

Request-Level Fields

FieldRequiredTypeDescription
request_idNostringIdentifier for the entire request (max 128 characters)
inputsYeslistMust contain exactly 1 input specification

Input-Level Fields

FieldRequiredTypeDefaultDescription
input_idNostring”input_id_0”Unique identifier for this structure prediction (max 128 characters)
moleculesYeslist-List of molecules to predict (minimum 1)
diffusion_samplesNointeger1Number of independent structures to generate (1-5)
output_formatNostring”cif”Output format: "cif" or "pdb"

Molecule Fields

FieldRequiredTypeValid ForDescription
typeYesstringAllMust be "protein", "dna", "rna", or "ligand"
sequenceConditionalstringProtein/DNA/RNAAmino acid or nucleotide sequence (1-4096 characters)
msaConditionaldictProtein/RNARequired for proteins (unless paired_msa provided) and RNA. Nested dict: database → format → AlignmentFileRecord
paired_msaNodictProteinJoint MSA for modeling chain-chain interactions
structural_templatesNolistProteinList of structural templates in CIF format to guide prediction
ccd_codesConditionalstringLigandCCD code (1-5 uppercase letters/numbers). Mutually exclusive with smiles
smilesConditionalstringLigandSMILES string. Mutually exclusive with ccd_codes
idNostring or listAllChain identifier(s). 1-4 alphanumeric characters each

AlignmentFileRecord Fields

FieldRequiredTypeDescription
alignmentYesstringMSA content as a string
formatYesstringFormat type: "a3m", "csv" (lowercase required)
rankNointegerOrdering rank for concatenating alignments (default: -1)

StructuralTemplate Fields

FieldRequiredTypeDescription
structureYesstringThe contents of the file containing the structural template, in CIF format
formatYesstringFormat type: must be "cif"
nameNostringOptional name to identify the template
chain_idNostringOptional chain ID to use from multi-chain CIF files. If not specified, the best matching chain is automatically selected. Supports CIF format (for example, ‘A’, ‘A1’, ‘B2’). 1–10 alphanumeric characters

Important Validation Rules

Ensure you follow these rules when specifying field values:

  • Protein MSAs: The first sequence in the MSA must exactly match the input protein sequence (without gaps)
  • RNA MSAs: The first sequence in the MSA must exactly match the input RNA sequence (without gaps)
  • Chain IDs: Must be 1-4 alphanumeric characters. PDB output format only supports single-character IDs
  • MSA Format Names: Must be lowercase ("a3m", not "A3M")
  • Ligand Specification: Must provide either ccd_codes or smiles, not both
  • Sequence Length: Maximum 4096 characters per molecule
  • Database Limit: Maximum 3 MSA databases per protein
  • Structural Templates: Only allowed for protein molecules and must be in CIF format