> This page is for MSA Search, version 2.3.0.
> For other versions, use one of these documentation indexes:
> - 2.6.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/msa-search/2.6.0/llms.txt
> - 2.5.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.5.0/llms.txt
> - 2.4.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.4.0/llms.txt
> - 2.3.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.3.0/llms.txt
> - 2.2.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.2.0/llms.txt
> - 2.1.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.1.0/llms.txt
> - 2.0.0: https://docs.nvidia.com/nim/bionemo/msa-search/2.0.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/msa-search/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.
# API Reference
This documentation contains the API reference for the MSA Search NIM.
## OpenAPI Specification
You can download or view the OpenAPI specification when the NIM is running:
```bash
curl http://localhost:8000/openapi.json
```
You can also navigate to the interactive API documentation at `http://localhost:8000/docs` in your browser.
## Multiple Sequence Alignment Search
Endpoint path: `/biology/colabfold/msa-search/predict`
Request type: `POST`
### Input Parameters
- `sequence` (string, **required**): A sequence to search against the MSA databases. Must be a valid protein sequence composed of the 20 standard amino acids plus X for unknown residues (ARNDCQEGHILKMFPSTWYVX). Length: 1-4096 characters.
Example:
```json
"SGSMKTAISLPDETFDRVSRRASELGMSRSEFFTKAAQR"
```
- `databases` (list[string], optional): Database names to search against. All databases are searched by default. **Database names are case-insensitive**; the response preserves the case you specify. Default: `["all"]`.
**Important**: For ColabFold search type, the first database in the list is used for profile generation. When using `["all"]`, uniref30 is automatically placed first.
Examples: `["all"]`, `["uniref30_2302"]`, `["uniref30_2302", "pdb70_220313"]`
- `search_type` (string, optional): Which type of MSA Search to run for alignment production. Default: `"colabfold"`.
Options:
- `"colabfold"`: Cascaded search with higher sensitivity. The first database is used for profile generation.
- `"alphafold2"`: Single-pass iterative search
Examples: `"colabfold"`, `"alphafold2"`
- `e_value` (float, optional): The e-value threshold for filtering hits when building the Multiple Sequence Alignment. Sequences with an e-value greater than this are not included in the MSA. Range: 0.0-1.0. Default: `0.0001`.
- `iterations` (int, optional): The number of MSA iterations to perform, where more iterations find more distant homologs. Default: `1`. **Note**: For cascaded search (`search_type="colabfold"`), the number of iterations is fixed to 3 and this parameter is ignored.
- `max_msa_sequences` (int, optional): Maximum sequences per individual database in the response (*N*). Each database's result is trimmed to at most *N* sequences. The merged `colabfold` entry is **not** trimmed. It concatenates untrimmed results from all *D* databases, so its size can be up to *D* × *U*, where *U* > *N*. The cascaded pipeline first accepts up to `max_accept` targets (default 100), then computes up to `alt_ali` alternative alignments per target (default 10), giving *U* ≤ `max_accept` × (1 + `alt_ali`) = 1100 with defaults. Configurable using `NIM_MMSEQS_PROFILE_ALIGN_MAX_ACCEPT` / `NIM_MMSEQS_FOLLOWUP_ALIGN_MAX_ACCEPT` and `NIM_MMSEQS_PROFILE_ALIGN_ALT_ALI` / `NIM_MMSEQS_FOLLOWUP_ALIGN_ALT_ALI`. Range: 1 to `NIM_GLOBAL_MAX_MSA_DEPTH` (default `500`). When GPU Server is enabled (default in version 2.0.0), the value must match `NIM_GLOBAL_MAX_MSA_DEPTH` exactly.
- `output_alignment_formats` (list[string], optional): The output format of the MSA. Supported formats: `"a3m"`, `"fasta"`. Default: `["a3m"]`.
Examples: `["a3m"]`, `["a3m", "fasta"]`
### Outputs
- `alignments` (Dictionary[string → Dictionary[string → AlignmentFileRecord]]): The MSA alignments organized by database and format. For example, `alignments["uniref30_2302"]["a3m"]` contains the A3M alignment for the uniref30 database. For colabfold search type, when multiple databases are searched, an additional `colabfold` key contains the merged alignment. When only a single database is searched, no `colabfold` key is present. The merged `colabfold` alignment concatenates results from all databases, so the query sequence appears once per source database. Unlike per-database entries, which are trimmed to `max_msa_sequences` N, the merged `colabfold` entry concatenates untrimmed results from all D databases, so its size can be up to D × U where U > N. The cascaded pipeline first accepts up to `max_accept` targets (default 100), then computes up to `alt_ali` alternative alignments per target (default 10), giving U ≤ `max_accept` × (1 + `alt_ali`) = 1100 with defaults. Refer to `NIM_MMSEQS_PROFILE_ALIGN_MAX_ACCEPT` / `NIM_MMSEQS_FOLLOWUP_ALIGN_MAX_ACCEPT` and `NIM_MMSEQS_PROFILE_ALIGN_ALT_ALI` / `NIM_MMSEQS_FOLLOWUP_ALIGN_ALT_ALI`. The merged result is not globally sorted by e-value; sequences from each database are sorted within their block, but blocks are concatenated in database order. The merged `colabfold` key is provided for compatibility and may be removed in a future release. Avoid using `colabfold` key, if possible.
- `metrics` (dictionary, optional): Contains information about the response useful for debugging and measuring performance. May be empty or null.
## Paired Multiple Sequence Alignment Search
Endpoint path: `/biology/colabfold/msa-search/paired/predict`
Request type: `POST`
Paired MSA search finds homologous sequences for each chain of a protein complex and pairs them by species, preserving co-evolutionary signals across chains. This is essential for accurate structure prediction of protein complexes.
### Input Parameters
- `sequences` (list[string] or dict[string, string], **required**): Protein sequences, one per chain. Must contain at least 2 sequences. Each sequence must be composed of the 20 standard amino acids plus X for unknown residues (ARNDCQEGHILKMFPSTWYVX). Can be provided as a list (chain IDs assigned automatically as identifiers such as "A" and "B") or as a dictionary keyed by chain ID.
Examples:
```json
["VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSH", "MHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPYTQRFFESFGDLST"]
```
```json
{"A": "VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSH", "B": "MHLTPEEKSAVTALWGKVNVDEVGGEALGRLLVVYPYTQRFFESFGDLST"}
```
- `databases` (list[string], optional): Databases to search against. Only databases with taxonomy information can be used for paired search. **Database names are case-insensitive**; the response preserves the case you specify. Default: `["all"]`.
Examples: `["all"]`, `["uniref30_2302"]`
- `e_value` (float, optional): The e-value threshold for filtering hits when building the Multiple Sequence Alignment. Sequences with an e-value greater than this are not included in the MSA. Range: 0.0-1.0. Default: `0.0001`.
- `max_msa_sequences` (int, optional): Maximum sequences per individual database per chain (*N*). Each database's result is trimmed to at most *N* sequences per chain. Only applies when `unpack=True`; when `unpack=False`, row count may exceed *N* as the cascaded search pipeline (expand + align) can produce up to *D* × *U* sequences where *D* is the number of databases and *U* ≤ `max_accept` × (1 + `alt_ali`) = 1100 with defaults (refer to monomer search documentation for details). Range: 1 to `NIM_GLOBAL_MAX_MSA_DEPTH` (default `500`). When GPU Server is enabled (default since version 2.0.0), the value must match `NIM_GLOBAL_MAX_MSA_DEPTH` exactly.
- `pairing_strategy` (string, optional): Pairing strategy for cross-chain sequence matching. Default: `"greedy"`.
Options:
- `"greedy"`: Maximizes rows by including species with partial chain coverage, pairing all chains that have hits and leaving gaps in others.
- `"complete"`: Only includes species where all chains have hits, producing fewer rows but with full coverage.
For 2-chain searches, both strategies produce identical results. Differences only arise for 3+ chains.
- `unpack` (boolean, optional): Controls output format. Default: `True`.
- `True`: Returns N alignments (one per chain) keyed by chain ID (for example, "A", "B"), each **strictly limited** to `max_msa_sequences` rows.
- `False`: Returns raw MMseqs2 output as a single "all_chains" alignment with chains concatenated using null-byte separators. Row count may exceed `NIM_GLOBAL_MAX_MSA_DEPTH` due to MMseqs2 internal pipeline behavior (prefilter limits are per-step, not strict global caps).
### Outputs
- `alignments_by_chain` (Dictionary[string → Dictionary[string → Dictionary[string → AlignmentFileRecord]]]): Paired MSA alignments organized by chain ID. When `unpack=True`, structure is `{chain_id: {database_name: {format: AlignmentFileRecord}}}`. When `unpack=False`, contains a single "all_chains" key with the concatenated paired alignment.
Example response (with `unpack=True`):
```json
{
"alignments_by_chain": {
"A": {
"uniref30_2302": {
"a3m": {
"alignment": ">A|-|A\nVLSPADKTNVKAAWGKV...\n>UniRef100_UPI00148F070C...\nVLSPAD...",
"format": "a3m"
}
}
},
"B": {
"uniref30_2302": {
"a3m": {
"alignment": ">B|-|B\nMHLTPEEKSAVTALWGKV...\n>UniRef100_UPI0008DEA318...\nMHLTPE...",
"format": "a3m"
}
}
}
},
"metrics": {}
}
```
To access the A3M-formatted alignment for chain A from the uniref30_2302 database:
```python
alignments_by_chain["A"]["uniref30_2302"]["a3m"]["alignment"]
```
- `metrics` (dictionary, optional): Contains information about the response useful for debugging and measuring performance. May be empty or null.
The output format for paired MSA search is always A3M.
## Structural Template Search
Endpoint path: `/biology/colabfold/msa-search/structure-templates/predict`
Request type: `POST`
Structural template search finds homologous protein structures by searching PDB-based databases and retrieves the corresponding mmCIF structure files. This endpoint combines MSA generation with template discovery in a single request, providing all inputs needed for template-based structure prediction.
### Input Parameters
- `sequence` (string, **required**): A protein sequence to search against the databases. Must be composed of the 20 standard amino acids plus X for unknown residues (ARNDCQEGHILKMFPSTWYVX). Length: 1-4096 characters.
Example:
```json
"SGSMKTAISLPDETFDRVSRRASELGMSRSEFFTKAAQR"
```
- `structural_template_databases` (list[string], optional): List of databases to search for structural templates. **Database names are case-insensitive**; the response preserves the case you specify. Default: value of `NIM_MSA_API_DEFAULT_STRUCTURAL_TEMPLATE_DBS` environment variable (typically `["pdb70_220313"]`).
Examples: `["pdb70_220313"]`, `["pdb70_220313", "pdb100_230517"]`
- `msa_databases` (list[string], optional): Database names to search for MSA generation. The first database is used for profile generation, which determines template search results. **Database names are case-insensitive**; the response preserves the case you specify. Default: `["all"]`.
Examples: `["all"]`, `["uniref30_2302"]`
- `e_value` (float, optional): The e-value threshold for filtering hits. Range: 0.0-1.0. Default: `0.0001`.
- `max_structures` (int, optional): Maximum number of PDB structures to return from template search. Default: `20`.
- `max_msa_sequences` (int, optional): Maximum sequences per individual database in the response (*N*). Each database's result is trimmed to at most *N* sequences. The merged `colabfold` entry is **not** trimmed. It concatenates untrimmed results from all *D* databases, so its size can be up to *D* × *U*, where *U* > *N*. The cascaded pipeline first accepts up to `max_accept` targets (default 100), then computes up to `alt_ali` alternative alignments per target (default 10), giving *U* ≤ `max_accept` × (1 + `alt_ali`) = 1100 with defaults. Configurable using `NIM_MMSEQS_PROFILE_ALIGN_MAX_ACCEPT` / `NIM_MMSEQS_FOLLOWUP_ALIGN_MAX_ACCEPT` and `NIM_MMSEQS_PROFILE_ALIGN_ALT_ALI` / `NIM_MMSEQS_FOLLOWUP_ALIGN_ALT_ALI`. Range: 1 to `NIM_GLOBAL_MAX_MSA_DEPTH` (default `500`). When GPU Server is enabled (default), the value must match `NIM_GLOBAL_MAX_MSA_DEPTH` exactly.
### Outputs
The response includes the same fields as the standard MSA search endpoint, plus template-specific outputs:
- `alignments`: MSA alignments organized by database and format (same as standard MSA search). Refer to [Outputs](#outputs) above for details on the merged `colabfold` entry.
- `search_hits` (Dictionary[string → Dictionary[string → SearchHitRecord]]): Structural template hits organized by database. Each entry contains template hits in M8 (BLAST tabular) format.
Each `SearchHitRecord` contains:
- `hits` (string): Template hits with columns: query, target, fident, alnlen, mismatch, gapopen, qstart, qend, tstart, tend, evalue, bits, and cigar. The output format can be customized using the `NIM_MMSEQS_TEMPLATE_CONVERTALIS_FORMAT` environment variable.
- `format` (string): Always `"m8"`.
- `structures` (Dictionary[string → StructuralTemplate]): Retrieved PDB structures for template hits, organized by PDB ID.
Each `StructuralTemplate` contains:
- `structure` (string): The mmCIF file content
- `format` (string): Always `"mmcif"`
- `metrics` (dictionary, optional): Performance and debugging information
Example response:
```json
{
"alignments": {
"uniref30_2302": {
"a3m": {
"alignment": ">query\nMVPSAGQLALF...",
"format": "a3m"
}
},
"colabfold": {
"a3m": {
"alignment": ">query\nMVPSAGQLALF...",
"format": "a3m"
}
}
},
"search_hits": {
"pdb70_220313": {
"m8": {
"hits": "query\t1abc_A\t85.0\t150\t...",
"format": "m8"
}
}
},
"structures": {
"1abc": {
"structure": "data_1ABC\n_entry.id 1ABC\n...",
"format": "mmcif"
}
},
"metrics": {}
}
```
Template search uses the ColabFold cascaded search approach internally. The profile generated from the first MSA database (for example, uniref30_2302) is used to find structural templates.
## Get Database Configuration
Endpoint path: `/biology/colabfold/msa-search/config/msa-database-configs`
Request type: `GET`
### Input Parameters
None.
### Outputs
Returns a list of database configuration objects, each containing:
- `name` (string): The database identifier used in API requests (for example, `uniref30_2302`).
- `display_name` (string): A human-readable display name suitable for UI presentation (for example, `UniProt Reference Clusters (30% identity) 2023-02`). Falls back to `name` for custom databases without a configured display name. Customizable using the `NIM_MSA_DB_DISPLAY_NAMES` environment variable or by mounting a custom `/opt/nim/msa/config.py`.
- `relative_path` (string, deprecated): Always `N/A`. Refer to the `/v1/metadata` endpoint.
- `index_relative_path` (string, deprecated): Always `N/A`. Refer to the `/v1/metadata` endpoint.
- `ngc_model` (string, deprecated): Always `N/A`. Refer to the `/v1/metadata` endpoint.
## Get MMSeqs2 Version
Endpoint path: `/biology/colabfold/msa-search/mmseqs2/version`
Request type: `GET`
### Input Parameters
None.
### Outputs
- `mmseqs2_version` (string): The version string of the MMSeqs2 installation, typically a git commit hash.
Use this endpoint to get the exact MMSeqs2 version when you need to create custom database indices. Custom indices must be created with the same MMSeqs2 version as the one running in the NIM to ensure compatibility.
## Health Endpoints
### Readiness Check
Endpoint path: `/v1/health/ready`
Request type: `GET`
**Description**: Checks if the service is ready to handle requests.
#### Outputs
- Status code `200`: Service is ready
- Status code `503`: Service is not ready
Response includes a JSON object with:
- `message` (string): Status message
- `object` (string): Always "health.response"
- `status` (string, optional): Status string for backwards compatibility
### Liveness Check
Endpoint path: `/v1/health/live`
Request type: `GET`
**Description**: Checks if the service is live (running).
#### Outputs
- Status code `200`: Service is live
- Status code `503`: Service is not live
Response format is the same as the readiness check.
## NIM Metadata Endpoints
### Version
Endpoint path: `/v1/version`
Request type: `GET`
**Description**: Returns version information for the NIM.
#### Outputs
- `release` (string): The product release version of the NIM
- `api` (string): The server API version running inside the NIM
### License
Endpoint path: `/v1/license`
Request type: `GET`
**Description**: Returns license information for the NIM.
#### Outputs
- `name` (string): The name of the license
- `path` (string): The filepath within the container containing the license content
- `sha` (string): SHA1 hash of the license contents
- `size` (integer): Number of characters in the license content
- `url` (string): URL where the license is hosted externally
- `type` (string): Always "file"
- `content` (string): The full license text
### Metadata
Endpoint path: `/v1/metadata`
Request type: `GET`
**Description**: Returns comprehensive metadata about the NIM deployment.
#### Outputs
- `assetInfo` (list[string]): Required container assets excluding model artifacts
- `licenseInfo` (LicenseEndpointModel): License information
- `modelInfo` (list[ModelInfo]): Information about models being served
- `repository_override` (string): Alternate location for retrieving artifacts
- `version` (string): NIM service version
- `selectedModelProfileId` (string): ID of the currently selected model profile
### Manifest
Endpoint path: `/v1/manifest`
Request type: `GET`
**Description**: Returns the manifest file describing required model artifacts.
#### Outputs
- `manifest_file` (string): Content of the manifest file
- `repository_override` (string): Alternate location for retrieving artifacts
### Metrics
Endpoint path: `/v1/metrics`
Request type: `GET`
**Description**: Exposes Prometheus metrics for monitoring.
#### Outputs
Returns metrics in Prometheus format.