Protein Sequence Similarity Search

Prerequisites

1. uv: Read the uv skill and follow its Setup instructions to ensure

uv is installed and on PATH.

1. User Notification: If LICENSE_NOTIFICATION.txt does not already exist in

this skill 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.

1. .env file: Make sure the .env file exists in your home directory.

Create one if it does not exist.

1. USER_EMAIL (optional but recommended): Recommended by the EBI for

BLAST job tracking, but the skill works without it. If the variable is missing from .env, do NOT ask the user to paste it into the chat (this would leak the value into the agent's context). Instead, give the user this command — substituting ENV_FILE with the resolved literal path to the .env file:

``bash printf "Enter contact email: " && read email && echo "USER_EMAIL=$email" >> "ENV_FILE" && echo "Saved." ``

The scripts load credentials automatically via dotenv. NEVER read, print, or inspect the .env file or its variables (e.g. no cat, grep, echo, printenv, or os.environ.get on keys). Credentials must stay out of the agent's context.

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").

1. 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).

1. Execute the MMseqs2 script:

• Default:

`` uv run scripts/mmseqs2_search.py -o -j ``

• With mgnify:

`` uv run scripts/mmseqs2_search.py -o -j --include-mgnify ``

1. The script will query the ColabFold MMseqs2 API and poll for completion.

This is typically fast (under 2 minutes).

1. 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."

1. 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.

1. Generate File Names: (e.g., proteinA_ebi_blast.json and

proteinA_ebi_blast.md).

1. This API requires the user email address to be set in the USER_EMAIL

environment variable for inclusion in request header.

1. Execute the BLAST script:

• Default (uniprotkb):

`` uv run scripts/uniprot_blast.py -o -j ``

• Custom database:

`` uv run scripts/uniprot_blast.py -o -j --databases ``

1. The script will query the EBI BLAST API and poll the server. Note: This

can take up to 15 minutes; wait patiently.

1. 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).

1. 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.

1. 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)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: google-deepmind
• Source: google-deepmind/science-skills
• License: Apache-2.0
• Homepage: https://antigravity.google/use-cases/science

Install and usage instructions live in the source repository linked above.