msa-search-nim

name	msa-search-nim
description	Generate multiple sequence alignments (MSAs) for protein sequences using the ColabFold MSA-Search NIM. Use for homolog search, UniRef30/ColabFold env searches, A3M or FASTA alignments, paired MSA search for complexes, PDB70 structural templates, hosted NVIDIA API calls, or local Docker deployment. For local deployment, download the databases in parallel with aria2c and launch via NIM_MODEL_NAME (the recommended default fast path, ~14 min vs over 80 min for the built-in downloader); a plain docker run uses the slow built-in downloader.
license	Apache-2.0 AND CC-BY-4.0
compatibility	requests>=2.28
allowed-tools	Bash, Read, Write, AskUserQuestion

MSA-Search NIM

Generate protein MSAs with GPU-accelerated MMSeqs2. Use this SKILL.md for first-pass hosted/local usage; load supplemental files only when needed:

• references/api.md: exact endpoints, schemas, Docker flags, response fields.
• references/science.md: MSA purpose, pairing/templates, limits, handoffs.
• references/parameters.md: database, pairing, depth, and template tuning.
• references/validation.md: alignment, template, and artifact checks.
• references/examples.md: compact hosted/local request patterns.

Choose Mode And Endpoint

Ask only when context is unclear:

Hosted NVIDIA API or local Docker NIM?

• Hosted standard MSA: https://health.api.nvidia.com/v1/biology/colabfold/msa-search/predict
• Hosted paired MSA: https://health.api.nvidia.com/v1/biology/colabfold/msa-search/paired/predict
• Local standard MSA: http://localhost:8000/biology/colabfold/msa-search/predict
• Local paired MSA: http://localhost:8000/biology/colabfold/msa-search/paired/predict
• Local templates: http://localhost:8000/biology/colabfold/msa-search/structure-templates/predict

Local inference paths do not include /v1/. Hosted requests use Authorization: Bearer $NGC_API_KEY. Supported local Docker startup uses NGC_API_KEY (or NVIDIA_API_KEY via the preflight) for registry login, entitlement checks, and first-run model downloads; pass it into the container with -e NGC_API_KEY. Local inference requests use no auth header after readiness. Warm-cache key-free startup varies by image/version and should not be assumed. The hosted template path returned HTTP 404 in validation, so use local Docker for template search unless the hosted docs/service changes.

Local Docker

Default local deployment = parallel download + NIM_MODEL_NAME. The first recipe below is the one to use for real workflows. It downloads the database(s) with a range-parallel downloader (aria2c) and starts the NIM against those files — ~14 min for UniRef30 vs >80 min for the NIM's built-in downloader (measured, H100). Do not reach for the plain docker run (the "Fallback" subsection) unless you only want a databases:pdb70 smoke test or you deliberately want the NIM to manage its own blob cache.

Local setup requires a GPU. Size the NVMe volume to the profile you pick (UniRef30 ~490 GB; full set ~1.4 TB). For setup answers, include env preflight, docker login, the parallel download, NIM_MODEL_NAME launch, readiness, and then no-auth local inference. Do not invent a cache default or drop the NVIDIA_API_KEY fallback.

# --- env preflight (do not drop the NVIDIA_API_KEY fallback) ---
set -a
[ -f .env ] && . ./.env
set +a
if [ -z "${NGC_API_KEY:-}" ] && [ -n "${NVIDIA_API_KEY:-}" ]; then
  export NGC_API_KEY="$NVIDIA_API_KEY"
fi
: "${NGC_API_KEY:?Set NGC_API_KEY or NVIDIA_API_KEY}"
: "${DB_DIR:=/data/fast-db}"          # where the parallel download lands

echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

# --- 1) pick the DB version(s) you need (paired/complex work = uniref30 only) ---
DB_VERSION=uniref30_2302-m18v1
command -v aria2c >/dev/null || { echo "aria2c required; install it (e.g. apt-get install -y aria2) and re-run"; exit 1; }
mkdir -p "$DB_DIR"

# --- 2) parallel download from NGC (see "Parallel Download" section for the all-DB loop) ---
curl -fsS -H "Authorization: Bearer $NGC_API_KEY" \
  "https://api.ngc.nvidia.com/v2/org/nim/team/colabfold/models/msa-search/${DB_VERSION}/files" \
  -o /tmp/files.json
DB_DIR="$DB_DIR" python3 - <<'PY'
import json, os
d = json.load(open("/tmp/files.json")); dbdir = os.environ["DB_DIR"]; lines = []
for url, path in zip(d[], d[]):
    lines += [url.strip(), f, f]
open(, ).write(.(lines) + )
PY
aria2c -i /tmp/aria.in --max-concurrent-downloads=4 --max-connection-per-server=16 \
  --=16 --min-split-size=1M --= --file-allocation=none


docker run -d --name msa-search --runtime=nvidia --gpus all \
  -e NGC_API_KEY \
  -e NIM_MODEL_NAME=/databases \
  -v  \
  -p 8000:8000 \
  nvcr.io/nim/colabfold/msa-search:2

Readiness:

until curl -sf http://localhost:8000/v1/health/ready; do sleep 5; done

If the DB is already present in $DB_DIR, skip steps 1-2 — the launch alone is a ~20 s warm start. See "Parallel Download For Any Database Set" for the multi-database (databases:all) loop and the full rationale.

Fallback: Let The NIM Download Its Own Databases (slower)

Use this only for a quick databases:pdb70 smoke test, or when you specifically want the NIM to manage its own blob cache. It uses the built-in downloader, which is slow on large profiles (UniRef30 stalled past 80 min in testing). Pin the smallest profile with NIM_MODEL_PROFILE (see "Faster Startup") so it does not fetch the full 1.4 TB.

: "${LOCAL_NIM_CACHE:?Set LOCAL_NIM_CACHE}"
mkdir -p "${LOCAL_NIM_CACHE}"; chmod 755 "${LOCAL_NIM_CACHE}"
docker run --rm --name msa-search \
  --runtime=nvidia --gpus all \
  -e NGC_API_KEY \
  -e NIM_MODEL_PROFILE=<hash-from-list-model-profiles> \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/colabfold/msa-search:2

Faster Startup: Task-Specific Database Profiles

The full database download is ~1.4 TB and can take well over an hour on first launch. If you only need some databases, select a task-specific profile so the NIM downloads just those. This is the single biggest lever on local startup time.

List the profiles your image actually ships (hashes change between releases — never hardcode them):

docker run --rm --entrypoint list-model-profiles nvcr.io/nim/colabfold/msa-search:2

Then pass the chosen hash with NIM_MODEL_PROFILE:

docker run --rm --name msa-search \
  --runtime=nvidia --gpus all \
  -e NGC_API_KEY \
  -e NIM_MODEL_PROFILE=<hash-from-list-model-profiles> \
  -v "${LOCAL_NIM_CACHE}:/opt/nim/.cache" \
  -p 8000:8000 \
  nvcr.io/nim/colabfold/msa-search:2

Profiles available in this image (confirm hashes with list-model-profiles):

Profile tags	Databases	Best for	Storage
databases:pdb70	PDB70	Quick testing / smoke check	~100 MB
databases:uniref30	UniRef30	Paired MSA search for complexes — UniRef30 is the only DB used for species-based pairing	~500 GB
databases:uniref30,pdb70,pdb	UniRef30 + PDB70 + PDB structures	Structural template search	~700 GB
databases:all (default)	UniRef30 + ColabFold envdb + PDB70 + PDB100 + PDB structures	Full sensitivity, all databases	~1.2 TB

Verify the loaded profile after readiness:

curl -s localhost:8000/v1/metadata | jq

Notes:

• The request-level databases parameter only selects among databases already downloaded; it does NOT change what is fetched at startup. Startup footprint is set by NIM_MODEL_PROFILE alone.
• Paired search needs UniRef30 only. colabfold_envdb_202108 has no taxonomy and cannot be used for pairing, so databases:uniref30 is the correct, smallest profile for complex/paired workflows — it skips the envdb, the largest part of the full set.
• For maximum monomer sensitivity (UniRef30 + envdb merged) you still need databases:all; there is no envdb-inclusive profile smaller than the full set.

Custom Or Individual Databases

To use a single manually downloaded database (or your own MMSeqs2 DB), download it from NGC and point the NIM at the mount with NIM_MODEL_NAME instead of a profile:

ngc registry model download-version nim/colabfold/msa-search:uniref30_2302-m18v1
# then mount the directory and set -e NIM_MODEL_NAME=/databases

NIM_MODEL_NAME replaces the profile databases entirely — the NIM uses only what is under that directory (discovered by scanning for **/*.idx). Mount multiple databases under one parent to combine them. NGC-downloaded databases are pre-indexed for GPU Server; custom databases must be indexed with mmseqs createindex first. Individually downloadable NGC model versions: uniref30_2302-m18v1, colabfold_envdb_202108-m18v1, pdb70_220313-m18v1, pdb100_230517-m18v1, pdb_20251028_zip-m18v1.

Recommended: Parallel Download For Any Database Set (Fast Deployment)

This is the recommended way to download the databases at all — for any profile, including the full databases:all set. Task-specific profiles cut what you download; this parallel downloader cuts how long that download takes. Use it whether you need one database or all of them. The gain is largest for the databases:uniref30 profile, which is ~490 GB dominated by two very large files (a ~241 GB GPU index and a ~134 GB sequence DB).

The NIM's built-in downloader parallelizes across files (max_parallel_files=10) but pulls each file over roughly one connection. The NGC CDN throttles a single connection to ~20–25 MB/s, so while the downloader is fetching one of the two giant files, most of its parallel slots sit idle and throughput collapses to that single-flow rate. Measured on an H100 node, the built-in path did not reach /health/ready in over 80 minutes.

A range-parallel downloader splits each file into many byte-range segments (the NGC CDN advertises accept-ranges: bytes), so a single 241 GB file is pulled over 16 connections at once — ~15× the single-flow rate. Same node, aria2c fetched the full ~490 GB in ~13.5 minutes.

Workflow (download once with aria2, then start the NIM against the files via NIM_MODEL_NAME):

# 1) Get presigned file URLs for the individual database model version from NGC.
#    (Requires NGC_API_KEY. The response arrays `urls` and `filepath` are positionally paired.)
curl -s -H "Authorization: Bearer $NGC_API_KEY" \
  'https://api.ngc.nvidia.com/v2/org/nim/team/colabfold/models/msa-search/uniref30_2302-m18v1/files' \
  -o files.json

# 2) Build an aria2 input file (URL + target filename per entry) and download in parallel.
python3 - <<'PY'
import json
d = json.load(open("files.json"))
lines = []
for url, path in zip(d["urls"], d["filepath"]):
    lines += [url.strip(), "  dir=/data/fast-db", f"  out={path}"]
open("aria.in", "w").write("\n".join(lines) + "\n")
PY
aria2c -i aria.in \
  --max-concurrent-downloads=4 --max-connection-per-server=16 --split=16 \
  --min-split-size=1M --continue=true --file-allocation=none

# 3) Start the NIM against the downloaded directory. NIM_MODEL_NAME makes the NIM discover
#    databases by scanning for **/*.idx, bypassing the profile/blob cache entirely.
docker run -d --name msa-search --runtime=nvidia --gpus all \
  -e NGC_API_KEY \
  -e NIM_MODEL_NAME=/databases \
  -v /data/fast-db:/databases \
  -p 8000:8000 \
  nvcr.io/nim/colabfold/msa-search:2

For all databases (equivalent to databases:all), repeat step 1 for each individual DB version and download them into sibling directories under one parent, then point NIM_MODEL_NAME at that parent — the NIM discovers every DB by scanning **/*.idx:

# fetch each DB's file list into /data/all-db/<db>/ ... then one aria2c per list, e.g.:
for V in uniref30_2302-m18v1 colabfold_envdb_202108-m18v1 pdb70_220313-m18v1 \
         pdb100_230517-m18v1 pdb_20251028_zip-m18v1; do
  curl -s -H "Authorization: Bearer $NGC_API_KEY" \
    "https://api.ngc.nvidia.com/v2/org/nim/team/colabfold/models/msa-search/$V/files" \
    -o "files_$V.json"
  # build an aria2 input from files_$V.json (dir=/data/all-db) and run aria2c on it
done
# then launch once against the parent:
#   docker run -d ... -e NIM_MODEL_NAME=/databases -v /data/all-db:/databases ...

The per-connection CDN throttle is the same for every database, so parallel download helps the full set proportionally — the more you download, the more absolute time it saves.

Notes:

• The presigned URLs expire (typically within a day) — build the aria2 input and start the download promptly after fetching files.json.
• Keep the downloaded directory's internal layout intact (e.g. uniref30_2302/…); the filepath values already encode it. The NIM needs the .idx file plus its companion files and the small .UNIREF30_READY / *.tar.gz.unpacked markers.
• The bottleneck is the CDN's per-connection cap, not local disk or CPU — a fast NVMe volume writes far faster than the network delivers. Raising --split / --max-connection-per-server helps only up to the node's aggregate egress ceiling.
• Best of all: download the profile once, then persist the cache volume (or this fast-db directory) and mount it on future nodes for a ~20 s warm start with no re-download.

Standard MSA Request

Use exact case-sensitive database names and response keys.

import os
import requests

HOSTED = True
url = (
    "https://health.api.nvidia.com/v1/biology/colabfold/msa-search/predict"
    if HOSTED else "http://localhost:8000/biology/colabfold/msa-search/predict"
)
headers = {"Content-Type": "application/json"}
if HOSTED:
    headers["Authorization"] = f"Bearer {os.getenv('NGC_API_KEY')}"

payload = {
    "sequence": "SGSMKTAISLPDETFDRVSRRASELGMSRSEFFTKAAQR",
    "databases": ["Uniref30_2302", "colabfold_envdb_202108"],
    "e_value": 0.0001,
    "output_alignment_formats": ["a3m"],
}
response = requests.post(url, headers=headers, json=payload, timeout=300)
response.raise_for_status()
result = response.json()

Paired MSA Request

Use paired search for protein complexes; payload field is sequences plural, and output is alignments_by_chain.

url = (
    "https://health.api.nvidia.com/v1/biology/colabfold/msa-search/paired/predict"
    if HOSTED else "http://localhost:8000/biology/colabfold/msa-search/paired/predict"
)
payload = {
    "sequences": [chain_a_sequence, chain_b_sequence],
    "e_value": 0.0001,
    "output_alignment_formats": ["a3m"],
}

Local Template Search

Use local Docker for structural templates. Set max_msa_sequences=500 unless NIM_GLOBAL_MAX_MSA_DEPTH was changed.

url = "http://localhost:8000/biology/colabfold/msa-search/structure-templates/predict"
headers = {"Content-Type": "application/json"}
payload = {
    "sequence": "VLSPADKTNVKAAWGKVGAHAGEYGAEALERMFLSFPTTKTYFPHFDLSHGSAQVKGHGKKVADALTNAVA",
    "structural_template_databases": ["pdb70_220313"],
    "max_structures": 20,
    "max_msa_sequences": 500,
}

Save Outputs

# Standard MSA: result["alignments"][database][format]["alignment"]
for db_name, formats in result.get("alignments", {}).items():
    for fmt_name, data in formats.items():
        with open(f"msa_{db_name}.{fmt_name}", "w", encoding="utf-8") as handle:
            handle.write(data["alignment"])

# Paired MSA: one alignment set per chain
for chain_id, chain_data in result.get("alignments_by_chain", {}).items():
    for db_name, formats in chain_data.items():
        for fmt_name, data in formats.items():
            with open(f"msa_chain_{chain_id}_{db_name}.{fmt_name}", "w", encoding="utf-8") as handle:
                handle.write(data["alignment"])

# Template search: save mmCIF structures and M8 hit tables
for name, cif in result.get("structures", {}).items():
    open(f"template_{name}.cif", "w", encoding="utf-8").write(cif)
for name, hit_table in result.get("search_hits", {}).items():
    open(f"template_hits_{name}.m8", , encoding=).write(hit_table)

A3M output can feed OpenFold3, AlphaFold2, or RoseTTAFold. For alignment depth, template, and sequence sanity checks, read references/validation.md.

Limits And Troubleshooting

• Sequence length: 1-4096 amino acids; X works since v2.3.0.
• max_msa_sequences: 1-500; local GPU server default must match NIM_GLOBAL_MAX_MSA_DEPTH.
• Paired MSA requires at least two sequences.
• Local URL 404 usually means an accidental /v1/ prefix.
• First local run can take hours while databases populate LOCAL_NIM_CACHE.