> This page is for OpenFold2, version 1.2.0. > For other versions, use one of these documentation indexes: > - 2.6.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/openfold2/2.6.0/llms.txt > - 2.5.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.5.0/llms.txt > - 2.4.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.4.0/llms.txt > - 2.3.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.3.0/llms.txt > - 2.2.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.2.0/llms.txt > - 2.1.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.1.0/llms.txt > - 2.0.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.0.0/llms.txt > - 1.2.0: https://docs.nvidia.com/nim/bionemo/openfold2/1.2.0/llms.txt > - 1.1.0: https://docs.nvidia.com/nim/bionemo/openfold2/1.1.0/llms.txt > - 1.0.0: https://docs.nvidia.com/nim/bionemo/openfold2/1.0.0/llms.txt > For clean Markdown of any page, append .md to the page URL. > For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt. > For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server. # Example Requests OpenFold2 NIM provides the following endpoint: - `biology/openfold/openfold2/predict-structure-from-msa-and-template`: Perform structure prediction from an input MSA and templates. ## Usage Below, we give real examples of requests that should run when the NIM is correctly configured. **Note**: We do not recommend submitting requests to this endpoint using the tool, curl, as its inputs have characters that require careful escaping in `bash`. We recommend interacting with this endpoint using the Python request module. ### Predict Structure The endpoint accepts requests where the data is formatted as in the [OpenApI Specification](api-reference.rst) Here is an example request using the Python `requests` module. - The field `sequence` is required. - The field `input_id` is optional but recommended. It serves as a unique identifier for tracking requests and correlating responses. Use formats like `"7WBN_A"` (PDB ID + chain) or custom names like `"my_protein_001"`. - The field `alignments` is optional, but omission can result in inaccurate structures. If included, the MSA content must be in `a3m` format. - The field `selected_models` is optional, but included here to reduce runtime, since the default specifies 5 models. - Use one of the following options if you want to include templates: - Set `use_templates`=`True`, and populate the field `templates` with the content of an `hhr` file, or several `hhr` files, which contain(s) HHSearch results. With these settings, template structures will be read from the database of structure files contained in the ngc model artifact. Template features will not be used if `use_templates`=`False`. - Set `use_templates`=`True`, and populate the field `explicit_templates` field with the content of one or more structure files in mmCIF format. Template features will not be used if `use_templates`=`False`. The following workflow is recommended when predicting structures for a given protein sequence. 1. Use other tools to produce multiple-sequence-alignments, and to put them in the `a3m` format. 2. Use other tools, and potentially the MSA results from step 1, to generate one of the following: - The template search results in an `hhr` file - The structural templates in **mmCIF** format that you can supply using the `mmcif_templates` field 3. Load the `a3m` files and `hhr` or `mmcif` files, in a Python script, and format the request as specified in the Open API specifications, included at the end of this documentation. - structural templates in **mmCIF** format that you can supply via the `explicit_templates` field. 3. Load the `a3m` files and `hhr` files, in a Python script, and format the request as specified in the Open API specifications, included at the end of this documentation. ```python import os import requests import json # -------------------------------- # parameters # -------------------------------- url = "http://localhost:8000/biology/openfold/openfold2/predict-structure-from-msa-and-template" headers = {"Content-Type": "application/json"} sequence = ( "GGSKENEISHHAKEIERLQKEIERHKQSIKKLKQSEQSNPPPNP" "EGTRQARRNRRRRWRERQRQKENEISHHAKEIERLQKEIERHKQSIKKLKQSEC" ) uniref90_alignment_in_a3m_trunc10=\ """>BQXYMDHSRWGGVPIWVK GGSKENEISHHAKEIERLQKEIERHKQSIKKLKQSEQSNPPPNPEGTRQARRNRRRRWRERQRQKENEISHHAKEIERLQKEIERHKQSIKKLKQSEC >UniRef90_A0A221IUG4 --------------------------QTVKLVKRLYQSNPPPNPEGTRQARRNRRRRWRERQRQ---------------------------------- >UniRef90_A7KWE0 ---------------------------TVRLIKQLYQSNPPPNPEGTRQARRNRRRRWRERQRQ---------------------------------- >UniRef90_A0A2I6UE91 ---------------------------TVKLIKEIYQSNPPPNPEGTRQARRNRRRRWRERQRQ---------------------------------- >UniRef90_D6NY33 ---------------------------AVRLIKQIYQSNPPPNPEGTRQARRNRRRRWRERQRQ---------------------------------- >UniRef90_A0A221IUJ5 --------------------------QTVKLIKRLYQSNPPPNPEGTRQARRNRRRRWREKQRQ---------------------------------- >UniRef90_D6NYR3 ---------------------------TVRLVKQLYQSNPPPNPEGTRQARRNRRRRWRERQRQ---------------------------------- >UniRef90_I6Y2C4 ---------------------------TVRLIKRIYQSNPPPNPEGTRQARRNRRRRWRERQRQIQN------------------------------- >UniRef90_A0A161CVP3 --------------------------QTIRLIKLLYQSNPPPNPEGTRQARRNRRRRWRERQRQ---------------------------------- >UniRef90_Q6EFX9 --------------------------QTVRLIKLLYQSNPPPNPEGTRQARRNRRRRWRERQRQ---------------------------------- >UniRef90_A0A2I6UAR5 --------------------------ETVKIIKYLYQSNPPPNPEGTRQARRNRRRRWRERQRQ---------------------------------- """ small_bfd_alignment_in_a3m = \ """>BQXYMDHSRWGGVPIWVK GGSKENEISHHAKEIERLQKEIERHKQSIKKLKQSEQSNPPPNPEGTRQARRNRRRRWRERQRQKENEISHHAKEIERLQKEIERHKQSIKKLKQSEC >A0A076V4A1_9HIV1 ------------------------------------QSNPPPNHEGTRQARRNRRRRWRERQRQ---------------------------------- """ # Example mmCIF template string (very small, minimal but valid) mmcif_template = """data_demo # loop_ _atom_site.group_PDB _atom_site.id _atom_site.type_symbol _atom_site.label_atom_id _atom_site.label_comp_id _atom_site.label_asym_id ATOM 1 N N ASN A """ # -------------------------------- # assemble request content # -------------------------------- data = { "sequence": sequence, "input_id": "example_protein_001", # Optional: unique identifier for request tracking "selected_models": [1, 2], "alignments": { "uniref90": { "a3m": { "alignment": uniref90_alignment_in_a3m_trunc10, "format": "a3m", } }, "small_bfd": { "a3m": { "alignment": small_bfd_alignment_in_a3m, "format": "a3m", } } }, "use_templates" : true, "explicit_templates": [ { "structure": mmcif_template, "format": "mmcif", "name": "demo_template", "source": "user_provided" } ], } # -------------------------------- # post-to-server # -------------------------------- response = requests.post( url=url, data=json.dumps(data), headers=headers, timeout=300, ) # Check if the request was successful if response.ok: print("Request succeeded:", response.json()) else: print("Request failed:", response.status_code, response.text) ``` The runtime for structure prediction is impacted by both sequence length and the number of sequences in the multiple-sequence-alignment. On an `NVIDIA H100 80GB HBM3` device, this example should complete in under 30 seconds.