/protein_sequence_similarity_search

name: protein-sequence-similarity-search
description: >
    Searches for homologous protein sequences using MMseqs2 (fast, default) or
    BLAST (comprehensive, fallback). Trigger this whenever the user provides a
    protein sequence or FASTA file and asks to find homologues, sequence
    matches, or wants to infer protein function based on sequence similarity,
    but not when the user wants to infer protein function based on structural
    similarity.

Prerequisites

1. **`uv`**: Read the `uv` skill and follow its Setup instructions to ensure `uv` is installed and on PATH. 2. **User Notification**: If .licenses/protein_sequence_similarity_search_LICENSE.txt does not already exist in the workspace root directory then (1) prominently notify the user to check the terms at https://www.ebi.ac.uk/jdispatcher/sss/ncbiblast and https://colabfold.com, then (2) create the file recording the notification text and timestamp. 3. **`.env` file**: Make sure the `.env` file exists in your home directory. Create one if it does not exist. 4. **`USER_EMAIL`** (optional but recommended): Recommended by the EBI for BLAST job tracking, but the skill works without it. You **MUST** use the safe credentials protocol in the `credentials` skill to check for and request this credential if this skill looks relevant to the user's request.

Goal

Take a user-provided amino acid sequence (or a path to a `.fasta` file), search for sequence homologues using the fastest available method, generate a Markdown-formatted table of the top hits, interpret key alignment metrics, summarize the inferred protein functions, and save results locally for future programmatic analysis.

Core Rules

• **Strict Validation**: For BLAST, only use database codes listed in the

table below.

• **No Hallucinations**: If a script throws an error or returns no hits,

inform the user clearly. Do NOT invent sequence homologues.

• **Do Not Parse Output Files**: Do not parse the JSON, a3m, or any other raw

output files. Rely on the generated `.md` file for your summary. The JSON and other outputs are for subsequent tool use only.

• **Always State the Method**: Every report must clearly state whether the

search used the quick MMseqs2 (ColabFold API) or the slower EBI BLAST method.

• **Notification**: If this skill is used, ensure this is mentioned in the

output. Explicitly state that the corresponding program (MMSEQS2 or EBI BLAST) and Sequence Databases were used.

Search Method Selection

Choose the search method based on the user's request:

If the **user says "quick search" or "fast search"**, **no specific method requested / general homologue search**, of if you are unsure: Run MMseqs2 (fast, default) using `mmseqs2_search.py`

If **MMseqs2 fails (exit code 2: RATELIMIT or API error)** or **User explicitly requests "BLAST"** or **a specific BLAST database** (e.g. `uniprotkb_swissprot`, `pdb`, `uniprotkb_human`): Run BLAST using `uniprot_blast.py`

Instructions

1. Identify the query from the user. It can be a raw sequence string (e.g., "MKVLY...") or a path to a local file (e.g., "./data/sequence.fasta").

2. **Determine the search method** using the list above.

Path A: MMseqs2 Search (Default)

1. **Generate File Names:** Generate descriptive output file names based on the input (e.g., `proteinA_mmseqs2.json` and `proteinA_mmseqs2.md`). 2. Execute the MMseqs2 script:

• **Default:**

    uv run scripts/mmseqs2_search.py <SEQUENCE_OR_FILE> -o <generated-filename.md> -j <generated-filename.json>

• **With mgnify:**

    uv run scripts/mmseqs2_search.py <SEQUENCE_OR_FILE> -o <generated-filename.md> -j <generated-filename.json> --include-mgnify

3. The script will query the ColabFold MMseqs2 API and poll for completion. This is typically fast (under 2 minutes).

4. **If the script exits with code 2** (API failure, rate limit), automatically fall back to BLAST (Path B below). Inform the user: "MMseqs2 search failed, falling back to BLAST."

5. **Read the Results:** Open and read the generated `.md` file.

Path B: BLAST Search (Explicit or Fallback)

1. **Database Selection & Validation:** Determine the most appropriate database(s) based on the user's prompt.

• Consult the **Available BLAST Databases** table below.
• If the user specifies a taxonomic group (e.g., "Find homologues in

microbes"), select the corresponding `Database Code` (e.g., `uniprotkb_bacteria`).

• If the user explicitly requests curated hits, use `uniprotkb_swissprot`.
• If no specific database is requested, do not specify `--databases`.
• **Validation:** Ensure the database code exactly matches an entry in the

table. If the user requests a database not on the list, **do not proceed** and provide the allowed list. 2. **Generate File Names:** (e.g., `proteinA_ebi_blast.json` and `proteinA_ebi_blast.md`). 3. This API requires the user email address to be set in the USER_EMAIL environment variable for inclusion in request header. You **MUST** use the safe credentials protocol in the `credentials` skill to check for and request this credential if this skill looks relevant to the user's request. 4. Execute the BLAST script:

• **Default (uniprotkb):**

    uv run scripts/uniprot_blast.py <SEQUENCE_OR_FILE> -o <generated-filename.md> -j <generated-filename.json>

• **Custom database:**

    uv run scripts/uniprot_blast.py <SEQUENCE_OR_FILE> -o <generated-filename.md> -j <generated-filename.json> --databases <db1,db2>

5. The script will query the EBI BLAST API and poll the server. **Note:** This can take up to 15 minutes; wait patiently.

6. **Read the Results:** Open and read the generated `.md` file.

Common Steps (Both Methods)

1. **Interpret the Metrics:** Su