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Protein Sequence Similarity Search

Google DeepMindOfficialPublished by Google DeepMind

  • Skill
  • Tool integration
  • Science and research
  • Apache-2.0
  • Low risk
  • Has scripts

From the author

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.
— the description by Google DeepMind

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill automates the process of identifying homologous protein sequences to help infer protein function. It provides a dual-method approach: a fast search using the MMseqs2 API (via ColabFold) for quick results, and a comprehensive search using the EBI BLAST API for more detailed analysis. The skill handles execution, manages file outputs, and assists in interpreting key alignment metrics such as E-value, query coverage, and sequence identity to provide a summary of potential protein functions.

What it does

  • Performs rapid protein sequence homology searches using MMseqs2.
  • Executes comprehensive BLAST searches against various UniProt databases.
  • Automatically falls back to BLAST if the MMseqs2 API is unavailable.
  • Generates structured Markdown reports and JSON files for all search results.
  • Interprets alignment metrics like E-value and sequence identity for the user.
  • Summarizes inferred protein functions based on identified homologues.

When to use it

  • When you need to find homologues for a specific amino acid sequence or FASTA file.
  • When you want to infer the function of a protein based on sequence similarity.
  • When you require a quick, high-level overview of potential protein matches.
  • When you need a deep, comprehensive search against specific taxonomic or curated databases.

Advantages

  • Flexible search options ranging from fast to comprehensive.
  • Supports a wide array of specialized UniProt and PDB databases.
  • Provides clear, human-readable summaries of complex alignment data.
  • Automates the polling and retrieval process for long-running BLAST jobs.

Usage examples

“Find homologues for this sequence: MKVLY...”
The agent runs an MMseqs2 search, identifies the top hits, and provides a summary of their functions and alignment statistics in a Markdown table.
“Perform a comprehensive BLAST search for my protein in the Swiss-Prot database.”
The agent executes the BLAST script using the uniprotkb_swissprot database and reports the most significant matches found.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google-deepmind/protein-sequence-similarity-search/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip protein-sequence-similarity-search.zip -d .agents/skills

Documentation by Google DeepMind

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: Summarize the top 3 to 5 sequence homologues. Assess match quality using:

    • Q-Cov (Query Coverage): High percentages mean the match covers most of the query sequence.

    • E-value: Lower E-values (e.g., 1e-50) indicate extreme statistical significance.

    • Seq Identity: Provides evolutionary context (highly conserved vs. distant homologue).

  2. Perform Functional Analysis:

    • If the results table includes protein descriptions, analyze them directly: report specific protein names/functions of the top homologues and summarize the variety of functions, domains, or protein families found.

    • If the results contain only UniProt accession IDs without descriptions (common with MMseqs2), look up the protein names and functions for the top 3–5 hits using the uniprot-database skill or other appropriate methods before summarizing.

  3. Inform the user of both newly created files (.json and .md) and their locations.

Available BLAST Databases

  • uniprotkb – UniProt Knowledgebase (The UniProt Knowledgebase includes UniProtKB/Swiss-Prot and UniProtKB/TrEMBL): The UniProt Knowledgebase (UniProtKB) is the central access point for extensive curated protein information, including function, classification, and cross-references. Search UniProtKB to retrieve "everything that is known" about a particular sequence

  • uniprotkb_swissprot – UniProtKB/Swiss-Prot (The manually annotated section of UniProtKB): The manually curated subsection of the UniProt Knowledgebase

  • uniprotkb_swissprotsv – UniProtKB/Swiss-Prot isoforms (The manually annotated isoforms of UniProtKB/Swiss-Prot): The isoform sequences for the manually curated subsection of the UniProt Knowledgebase

  • uniprotkb_reference_proteomes – UniProtKB Reference Proteomes: Taxonomic subset of the UniProtKB Reference Proteomes

  • uniprotkb_trembl – UniProtKB/TrEMBL (The automatically annotated section of UniProtKB): Subsection of the UniProt Knowledgebase derived from ENA Sequence (formerly EMBL-Bank) coding sequence translations with annotation produced by an automated process

  • uniprotkb_refprotswissprot – UniProtKB Reference Proteomes plus Swiss-Prot: UniProtKB Reference Proteomes plus Swiss-Prot

  • uniprotkb_archaea – UniProtKB Archaea: Taxonomic subset of the UniProt Knowledgebase for archaea

  • uniprotkb_arthropoda – UniProtKB Arthropoda: Taxonomic subset of the UniProt Knowledgebase for arthropoda

  • uniprotkb_bacteria – UniProtKB Bacteria: Taxonomic subset of the UniProt Knowledgebase for bacteria

  • uniprotkb_complete_microbial_proteomes – UniProtKB Complete Microbial Proteomes: Taxonomic subset of the UniProt Knowledgebase for complete microbial proteomes

  • uniprotkb_eukaryota – UniProtKB Eukaryota: Taxonomic subset of the UniProt Knowledgebase for eukaryota

  • uniprotkb_fungi – UniProtKB Fungi: Taxonomic subset of the UniProt Knowledgebase for fungi

  • uniprotkb_human – UniProtKB Human: Taxonomic subset of the UniProt Knowledgebase for human

  • uniprotkb_mammals – UniProtKB Mammals: Taxonomic subset of the UniProt Knowledgebase for mammals

  • uniprotkb_nematoda – UniProtKB Nematoda: Taxonomic subset of the UniProt Knowledgebase for nematoda

  • uniprotkb_rodents – UniProtKB Rodents: Taxonomic subset of the UniProt Knowledgebase for rodents

  • uniprotkb_vertebrates – UniProtKB Vertebrates: Taxonomic subset of the UniProt Knowledgebase for vertebrates

  • uniprotkb_viridiplantae – UniProtKB Viridiplantae: Taxonomic subset of the UniProt Knowledgebase for viridiplantae

  • uniprotkb_viruses – UniProtKB Viruses: Taxonomic subset of the UniProt Knowledgebase for viruses

  • uniprotkb_enzyme – UniProtKB Enzyme: Taxonomic subset of the UniProt Knowledgebase for enzymes

  • uniprotkb_covid19 – UniProtKB COVID-19: Taxonomic subset of the UniProt Knowledgebase for COVID-19

  • uniref100 – UniProt Clusters 100% (UniRef100): The UniProt Reference Clusters (UniRef) containing sequences which are 100% identical.

  • uniref90 – UniProt Clusters 90% (UniRef90): The UniProt Reference Clusters (UniRef) containing sequences which are 90% identical.

  • uniref50 – UniProt Clusters 50% (UniRef50): The UniProt Reference Clusters (UniRef) containing sequences which are 50% identical.

  • pdb – Protein Structure Sequences (PDBe protein structure sequences): Protein sequences from structures described in the Brookhaven Protein Data Bank (PDB)

Files

scripts/uniprot_blast.py

# Copyright 2026 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""Runs EBI BLAST for a FASTA file or raw sequence string against UniProt.

Writes a Markdown-formatted results table to the file specified by the
required --output flag. Optionally saves raw JSON via -j/--json.
"""

# /// script
# requires-python = ">=3.10"
# dependencies = [
#   "polite-http",
#   "python-dotenv",
# ]
# ///

import argparse
import json
import os
import sys
import time
import urllib.parse

import dotenv
from polite_http import http_client

ALLOWED_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",
]

MAX_ALIGNMENT_HITS = 300
DEFAULT_EVALUE = 1000
POLLING_TIMEOUT = 15 * 60  # 15 minutes.
BASE_URL = "https://www.ebi.ac.uk/Tools/services/rest/ncbiblast"
CLIENT = http_client.HttpClient(BASE_URL, qps=2)


def search_uniprot(query_input, output_md, json_file, databases):
  """Runs EBI BLAST search, writes results to output files."""
  # Redirect all print output to the .md file
  original_stdout = sys.stdout
  with open(output_md, "w") as md_file:
    sys.stdout = md_file
    try:
      _run_blast(query_input, json_file, databases, tty=original_stdout)
    finally:
      sys.stdout = original_stdout

  print(f"[*] Results saved to: {output_md}")
  if json_file:
    print(f"[*] Results saved to: {json_file}")
  print("...done!")


def _run_blast(query_input, json_file, databases, tty):
  """Internal: runs the actual BLAST search with print going to file."""

  # Process and validate the databases
  selected_dbs = [db.strip().lower() for db in databases.split(",")]
  invalid_dbs = [db for db in selected_dbs if db not in ALLOWED_DATABASES]

  if invalid_dbs:
    print(f"[!] Error: Invalid database(s) provided: {', '.join(invalid_dbs)}")
    print(f"[*] Allowed databases are: {', '.join(ALLOWED_DATABASES)}")
    print(
        f"[!] Error: Invalid database(s) provided: {', '.join(invalid_dbs)}",
        file=tty,
    )
    sys.exit(1)

  # Determine if input is a file path or a raw sequence string
  if os.path.isfile(query_input):
    print(f"[*] Reading sequence from file: {query_input}")
    with open(query_input, "r") as f:
      sequence = f.read().strip()
      # If it's a fasta file with a header, skip the header line
      if sequence.startswith(">"):
        sequence = "".join(sequence.split("\n")[1:])
  else:
    print("[*] Using raw sequence string provided via command line.")
    sequence = query_input.strip()

  if not sequence:
    print("[!] Error: Empty sequence provided.")
    print("[!] Error: Empty sequence provided.", file=tty)
    sys.exit(1)

  # Calculate query length for later Q-Cov math
  q_len = len(sequence)

  print("[*] Search method: EBI BLAST")
  print(f"[*] Submitting sequence ({q_len} residues) to EBI NCBI BLAST API...")
  print(f"[*] Searching databases: {', '.join(selected_dbs)}")
  print(
      f"[*] Search method: EBI BLAST — databases: {', '.join(selected_dbs)}",
      file=tty,
  )

  # 1. Submit Job
  url_run = f"{BASE_URL}/run"
  params = {
      "program": "blastp",
      "stype": "protein",
      "sequence": sequence,
      "database": ",".join(selected_dbs),
  }
  user_email = os.environ.get("USER_EMAIL")
  if user_email:
    params["email"] = user_email
  else:
    print(
        "[!] Warning: USER_EMAIL environment variable not set. Request may"
        " fail."
    )
  data = urllib.parse.urlencode(params).encode("utf-8")
  try:
    job_id = CLIENT.fetch_text(url_run, method="POST", data=data).strip()
    print(f"[*] Job ID generated: {job_id}")
    print(f"[*] Job ID: {job_id}", file=tty)
  except (
      http_client.HttpError,
      TimeoutError,
      RuntimeError,
      IOError,
  ) as e:
    print(f"[!] API Submission Failed: {e}")
    print(f"[!] API Submission Failed: {e}", file=tty)
    sys.exit(1)
  # 2. Poll the server until the search finishes
  print("[*] Polling server for completion (this may take a few minutes)...")
  print("[*] Polling server for completion...", file=tty)
  url_status = f"{BASE_URL}/status/{job_id}"

  start_time = time.monotonic()
  while time.monotonic() - start_time < POLLING_TIMEOUT:

    try:
      status = CLIENT.fetch_text(url_status, timeout=20).strip()

      if status == "FINISHED":
        print("\n[*] Job marked as FINISHED.")
        print("\n[*] Job marked as FINISHED.", file=tty)
        break
      elif status in ["ERROR", "FAILURE", "NOT_FOUND"]:
        print(f"\n[!] BLAST job failed with status: {status}")
        print(f"\n[!] BLAST job failed with status: {status}", file=tty)
        sys.exit(1)

      sys.stdout.write(".")
      sys.stdout.flush()
      tty.write(".")
      tty.flush()
      time.sleep(30)

    except (
        http_client.HttpError,
        TimeoutError,
        RuntimeError,
    ) as e:
      print(f"\n[!] Polling error: {e}")
      print(f"\n[!] Polling error: {e}", file=tty)
      sys.exit(1)

  else:
    print("\n[!] Polling timed out.")
    print("\n[!] Polling timed out.", file=tty)
    sys.exit(1)

  time.sleep(3)

  # 3. Fetch and format the results
  print("\n[*] Job complete. Fetching results...\n")
  try:
    res = CLIENT.fetch_json(f"{BASE_URL}/result/{job_id}/json", timeout=60)
  except (
      http_client.HttpError,
      TimeoutError,
      RuntimeError,
      json.JSONDecodeError,
      IOError,
  ) as e:
    print(f"[!] Failed to fetch or parse results: {e}")
    sys.exit(1)

  # Save JSON to file (optional)
  if json_file:
    with open(json_file, "w") as f:
      json.dump(res, f, indent=4)
    print(f"[*] Raw JSON results successfully saved to: {json_file}\n")

  # Output as a Markdown Table for LLM Agent parsing
  print(f"### Top {MAX_ALIGNMENT_HITS} Sequence Homologues (EBI BLAST)")
  print("| Target ID | Q-Cov | E-value | Seq Identity(%) | Aln Length |")
  print("|---|---|---|---|---|")

  # The EBI JSON wraps hits in a 'hits' array
  hits = res.get("hits", [])

  if not hits:
    print("[!] No homologues found.")
    return

  for hit in hits[:MAX_ALIGNMENT_HITS]:
    target_acc = hit.get("hit_acc", "N/A")
    target_desc = hit.get("hit_desc", hit.get("hit_def", ""))

    # Combine Accession and Description for the LLM
    target = f"{target_acc} {target_desc}".strip()

    hsps = hit.get("hit_hsps", [{}])
    best_hsp = hsps[0] if hsps else {}

    # Calculate Query Coverage
    hsp_query_from = int(best_hsp.get("hsp_query_from", 0))
    hsp_query_to = int(best_hsp.get("hsp_query_to", 0))

    if q_len > 0 and hsp_query_to > hsp_query_from:
      aligned_q_residues = (hsp_query_to - hsp_query_from) + 1
      cov_percentage = min((aligned_q_residues / q_len) * 100, 100.0)
      q_cov = f"{cov_percentage:.1f}%"
    else:
      q_cov = "N/A"

    evalue = str(best_hsp.get("hsp_expect", "N/A"))
    seq_id = str(best_hsp.get("hsp_identity", "N/A")) + "%"
    aln_len = str(best_hsp.get("hsp_align_len", "N/A"))

    # Print Markdown row without truncation
    print(f"| {target} | {q_cov} | {evalue} | {seq_id} | {aln_len} |")


def main():
  dotenv.load_dotenv(os.path.expanduser("~/.env"))
  parser = argparse.ArgumentParser(
      description=(
          "Query EBI NCBI BLAST with a FASTA file or raw sequence string."
      )
  )
  parser.add_argument(
      "query_input", help="Path to the FASTA file or raw sequence string"
  )
  parser.add_argument(
      "-o",
      "--output",
      required=True,
      help="Path to save the output Markdown (.md) file (required)",
  )
  parser.add_argument(
      "-j",
      "--json",
      help="Path to save the output JSON file (optional)",
      default=None,
  )
  parser.add_argument(
      "--databases",
      help="Comma-separated list of databases to search",
      default="uniprotkb",
  )
  args = parser.parse_args()

  print(f"[*] Output: {args.output}")
  if args.json:
    print(f"[*] Output: {args.json}")

  search_uniprot(args.query_input, args.output, args.json, args.databases)


if __name__ == "__main__":
  main()

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs Python

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Attribution and licence

Published by
Google DeepMind
Publisher
Google DeepMind (Official)
Source
google-deepmind/science-skills · skills/protein_sequence_similarity_search · commit 4de6786
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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