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Protein Sequence Similarity Search

Organization
ai4protein
protein_sequence_similarity_search

Find homologous protein sequences from a query sequence using MMseqs2 (fast, ColabFold web API) or BLAST (comprehensive, EBI). Use when the user provides a protein sequence or FASTA file and wants homologs, function inference by sequence similarity, or input for an MSA. Do NOT use for structural similarity (use foldseek) or DNA/RNA queries.

Overview

Publisherai4protein
RepositoryVenusFactory2
Skill nameprotein_sequence_similarity_search
Stars
255
Forks
37
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by ai4protein on GitHub. Read the source before you install it.

Installation

Install the Protein Sequence Similarity Search AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/ai4protein/VenusFactory2/tree/main/src/agent/skills/protein_sequence_similarity_search
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/ai4protein/VenusFactory2.git /tmp/VenusFactory2
mkdir -p .claude/skills
cp -r /tmp/VenusFactory2/src/agent/skills/protein_sequence_similarity_search .claude/skills/protein_sequence_similarity_search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Protein Sequence Similarity Search in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Protein Sequence Similarity Search on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Protein Sequence Similarity Search is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Protein Sequence Similarity Search

Overview

Two complementary search engines, each wrapped as a submit-poll-download-parse pipeline:

  • MMseqs2 (ColabFold) — fast, default. Searches UniRef (+ optional MGnify environmental). Best for quickly building an MSA-grade hit set.
  • EBI BLAST (NCBI BLAST hosted at EBI) — slower but more exhaustive search across multiple UniProt/UniRef/PDB databases. Use when you need BLAST-specific scoring (e.g., comparing against a published BLAST result) or when MMseqs2 returns few hits.

Both tools save the full hit list to a JSON file on disk and return only a top-10 markdown preview in the response, keeping the agent's context window small.

Project Tools (VenusFactory2)

ToolArgsReturnsDescription
download_mmseqs2_homologs_by_sequencesequence_or_fasta_path (required, raw sequence or FASTA file path), out_dir (required), include_mgnify (default False), poll_interval (default 10.0 s), timeout_secs (default 900 s)JSON: {status, file_info {file_path -> mmseqs2_<ticket>.json, file_size, format: "json"}, content_preview (top-10 markdown table), biological_metadata {engine, ticket_id, query_length, hit_count, include_mgnify}}Fast UniRef homologue search via ColabFold MMseqs2 API.
download_blast_homologs_by_sequencesequence_or_fasta_path (required), out_dir (required), database (default "uniprotkb_swissprot"; comma-separated list ok), email (optional, falls back to env USER_EMAIL), poll_interval (default 30.0 s), timeout_secs (default 900 s)JSON: {status, file_info {file_path -> blast_<jobid>.json}, content_preview, biological_metadata {engine, job_id, query_length, databases, hit_count, email}}Authoritative BLAST search against UniProt/UniRef/PDB at EBI.

When to Use This Skill

  • The user gives a sequence and asks "find similar proteins" / "what's this protein's family" / "find homologs"
  • You need to build an MSA from a single seed sequence: run MMseqs2 → write hits → download_clustalo_msa_by_fasta
  • You need orthologs in a specific clade: BLAST with database=uniprotkb_human (or _bacteria, _viruses, etc.)
  • You need PDB hits to seed structure-based analysis: BLAST with database=pdb

Which Engine to Pick

SituationEngine
Default / unspecifiedMMseqs2 (faster, ~1-3 min)
User explicitly says "BLAST"BLAST
MMseqs2 returned <5 hitsBLAST fallback
Need PDB-only hitsBLAST with database=pdb
Need MGnify environmental hitsMMseqs2 with include_mgnify=True
Want comprehensive UniProtKB+TrEMBL coverageBLAST with database=uniprotkb

Supported BLAST Databases

uniprotkb uniprotkb_swissprot uniprotkb_swissprotsv uniprotkb_reference_proteomes uniprotkb_trembl uniprotkb_refprotswissprot uniprotkb_archaea uniprotkb_arthropoda uniprotkb_bacteria uniprotkb_complete_microbial_proteomes uniprotkb_eukaryota uniprotkb_fungi uniprotkb_human uniprotkb_mammals uniprotkb_nematoda uniprotkb_rodents uniprotkb_vertebrates uniprotkb_viridiplantae uniprotkb_viruses uniprotkb_enzyme uniprotkb_covid19 uniref100 uniref90 uniref50 pdb

Output JSON Schema (file_info.file_path)

json
{
  "hits": [
    {"target_id": "...", "q_cov": 78.3, "e_value": 1.2e-50, "identity": 0.45, "aln_len": 240, ...},
    ...
  ],
  "metadata": {"engine": "MMseqs2 (ColabFold)", "ticket_id": "...", "query_length": 256, "hit_count": 178, ...}
}

Rate Limiting

  • ColabFold MMseqs2: 2 req/s, ~1-3 min wall clock per job. Polls every 10 s.
  • EBI BLAST: 2 req/s submit; polls every 30 s. Jobs take 2-10 min.
  • Both timeout at 15 min by default — increase timeout_secs for very large queries.

Common Mistakes

  • Passing a FASTA file with multiple records but expecting all to be searched: only the first record is used. Loop in the agent if you need multiple queries.
  • Skipping out_dir: required. Tools error with ValidationError: empty out_dir.
  • Setting database to an unsupported value: returns ValidationError with the full allowed list — pick from that list.
  • Treating MMseqs2 hits as the final answer when count is 0: this is a soft failure; fall back to BLAST.

References

Frequently asked questions

What does the Protein Sequence Similarity Search AI skill do?

Find homologous protein sequences from a query sequence using MMseqs2 (fast, ColabFold web API) or BLAST (comprehensive, EBI). Use when the user provides a protein sequence or FASTA file and wants homologs, function inference by sequence similarity, or input for an MSA. Do NOT use for structural similarity (use foldseek) or DNA/RNA queries.

Why use Protein Sequence Similarity Search on TypingMind?

Because you install it once and use it with any model. Protein Sequence Similarity Search is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Protein Sequence Similarity Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ai4protein/VenusFactory2/tree/main/src/agent/skills/protein_sequence_similarity_search. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Protein Sequence Similarity Search?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Protein Sequence Similarity Search?

As many as you like. As long as a model supports skills, you can use Protein Sequence Similarity Search with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Protein Sequence Similarity Search AI skill free?

It is published on GitHub by ai4protein. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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