Download skills/bionemo-msa-search-nim/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does msa-search-nim live?
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Which agents can use msa-search-nim?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
>
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
permissions:
env # reads NGC_API_KEY/NVIDIA_API_KEY and local NIM setup variables
network # hosted MSA requests and documented NGC/local NIM setup
MSA-Search NIM
Generate protein MSAs with GPU-accelerated MMSeqs2. Use this guide for
first-pass hosted/local usage; load supplemental files only when needed:
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="$DB_DIR" python3 - <<'PY'
import json, os
d = json.load(open("/tmp/files.json")); dbdir = os.environ["DB_DIR"]; lines = []
for url, path in zip(d["urls"], d["filepath"]):
lines += [url.strip(), f" dir={dbdir}", f" out={path}"]
open("/tmp/aria.in", "w").write("\n".join(lines) + "\n")
PY
aria2c -i /tmp/aria.in --max-concurrent-downloads=4 --max-connection-per-server=16 \
--split=16 --min-split-size=1M --continue=true --file-allocation=none
# --- 3) launch the NIM against the downloaded files (skips the slow built-in download) ---
docker run -d --name msa-search --runtime=nvidia --gpus all \
-e NGC_API_KEY \
-e NIM_MODEL_NAME=/databases \
-v "${DB_DIR}:/databases" \
-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.
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
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_NAMEreplaces 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.
For a hosted standard search, run the bundled client from this skill's directory.
It submits the real request, validates both database results, and saves the raw
JSON and A3M files. Choose a new output directory for each run:
The client reads NGC_API_KEY or NVIDIA_API_KEY from the environment. It permits
at most two requests, each with a 10-second connection timeout and a 300-second
read timeout, with five seconds between attempts. If it exits nonzero, report the
service failure and stop. Do not restart it repeatedly, extend timeouts beyond the
task budget, or replace the missing response with synthetic alignments.
The underlying request format, also usable with a running local NIM, is:
# 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", "w", encoding="utf-8").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.
Hosted HTTP 502/503/504 or repeated read timeouts indicate that the hosted
request did not complete. Check service availability after the bounded retry;
a longer client timeout cannot fix a server-generated HTTP 504.
Do not invent a polling URL for health.api.nvidia.com. The published standard
MSA example uses synchronous POST; a pending response needs a documented
service-specific completion mechanism before it can count as a result.
How do I install msa-search-nim?
Download skills/bionemo-msa-search-nim/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does msa-search-nim live?
The publisher keeps it at https://github.com/NVIDIA/skills.
Which agents can use msa-search-nim?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.