Protein Sequence Msa
Google DeepMindOfficialPublished by Google DeepMind
- Skill
- Tool integration
- Science and research
- Apache-2.0
- Low risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured method for performing multiple sequence alignment (MSA) on protein sequences using the EBI Clustal Omega service. It includes a wrapper script to manage API interactions and provides guidance on interpreting alignment results, such as calculating identity metrics and mapping functional residues across a set of sequences.
What it does
- Aligns two or more protein sequences provided in FASTA format.
- Uses the EBI Clustal Omega API via a provided wrapper script.
- Saves alignment results locally for further analysis.
- Supports calculation of various identity metrics including sequence coverage and global identity.
- Facilitates mapping of known functional sites to alignment column indices.
- Ensures API usage adheres to specified rate limits.
When to use it
- When you need to assess similarity between multiple protein sequences.
- When identifying conserved domains or key residues within a protein family.
- When you have a FASTA file containing between 2 and 4000 protein sequences.
- When you need to quantify evolutionary signatures across a set of proteins.
Advantages
- Automates the interaction with the EBI Clustal Omega service.
- Provides standardized metrics for evaluating sequence relationships.
- Includes guidance on selecting appropriate identity calculations based on biological context.
- Enforces rate limits to respect external service terms.
Usage examples
- “Align these three protein sequences in my fasta file and tell me how conserved the active site is.”
- The agent runs the alignment script, identifies the active site residues, and reports on their conservation across the provided sequences.
- “Calculate the overlap identity for my protein alignment file.”
- The agent processes the alignment and computes the identity metric by excluding terminal gaps from the total column count.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google-deepmind/protein-sequence-msa/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip protein-sequence-msa.zip -d .agents/skills
Documentation by Google DeepMind
Prerequisites
uv: Read theuvskill and follow its Setup instructions to ensureuvis installed and on PATH.User Notification: If .licenses/protein_sequence_msa_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/msa/clustalo and https://www.ebi.ac.uk/about/terms-of-use/, then (2) create the file recording the notification text and timestamp.
.envfile: Make sure the.envfile exists in your home directory. Create one if it does not exist.USER_EMAIL: Required by the wrapper script for Clustal Omega job tracking (recommended by the EBI). You MUST use the safe credentials protocol in thecredentialsskill to check for and request this credential if this skill looks relevant to the user's request.
Core Rules
Use the Wrapper: ALWAYS execute the alignment using
scripts/msa_align.pyrather than writing your own curl or custom Python requests. The script automatically enforces the required rate limit to respect EBI's Terms of Use.Notification: If this skill is used, ensure this is mentioned in the output.
Always state the method: Every report must clearly state that the alignment was performed using EBI Clustal Omega.
No Hallucinations: Do NOT invent alignments or conservation metrics. Report only what is present in the alignment file.
Goal
Take a file containing multiple protein sequences in FASTA format, perform multiple sequence alignment using the EBI Clustal Omega API, save the resulting alignment locally for future programmatic analysis, and interpret the results towards addressing the user's specific research objective (e.g., assessing similarity, identifying conserved domains, or analyzing key residues).
Instructions
Prepare Input File: The input must be a plain text file containing two or more protein sequences in FASTA format. Each sequence header must start with a
>symbol. Example:>Sequence_1_Name MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQ QRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG >Sequence_2_Name MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQ QRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGGExecute Alignment: Run the alignment script:
uv run scripts/msa_align.py <INPUT_FASTA> -o <OUTPUT_FILE>Always specify the output file with
-oor--output.Interpret and Report Results: Analyze the Clustal Omega alignment by selecting metrics and mapping strategies aligned with the research objective. Note that while Clustal Omega produces a Global Alignment, pairwise metrics can be extracted to evaluate specific relationships within the set:
Identity Metric Options: The choice of denominator determines how insertions/deletions (gaps) affect the final percentage. Select the most appropriate calculation based on the biological context:
Pairwise - Sequence Coverage:
(Identical Residue Matches) / (Length of Shorter Sequence). Use when determining if a specific domain or fragment is fully preserved within a larger protein. This ignores gaps in the longer sequence, focusing purely on the "content" of the shorter one.Pairwise - Global Identity:
(Identical Residue Matches) / (Total Alignment Columns). Use when comparing full-length sequences of similar expected length. This is the most conservative metric; it penalizes for all gaps (indels) introduced by any sequence in the MSA.Pairwise - Overlap Identity:
(Identical Residue Matches) / (Total Alignment Columns - Terminal Gaps). Use when comparing a fragment to a full-length protein or when sequences have long unaligned "tails." This focuses on similarity only where the sequences physically overlap.Multisequence - Conservation Index:
(Fully Conserved Columns) / (Total Alignment Columns). Use for quantifying the percentage of residues that are 100% identical across the entire alignment set. This identifies the core evolutionary signature of the protein family.
Feature Mapping: Leverage known biological data from specific sequences to ground the analysis:
Knowledge Gathering: Identify relevant known sites or regions (e.g., catalytic residues, binding motifs) from your input or via external tools.
Coordinate Projection: Map these features onto the corresponding Column Indices of the alignment.
Targeted Discussion: Use these columns to drive the assessment:
Local Conservation: Analyze if the known functional residues are invariant across the set.
Region-Specific Metrics: Calculate identity/similarity specifically within the mapped functional regions rather than the whole sequence.
Goal Contribution: Discuss how this data contributes to your goal, e.g. using conservation to corroborate a prediction or divergence to reject a functional hypothesis.
References
Multiple Sequence Alignment: https://www.ebi.ac.uk/jdispatcher/msa/clustalo
Files
- ATTRIBUTION.mdAdded by AutoGineer922 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.0 KiB
- references/citation.bib4.6 KiB
- scripts/msa_align.pyScript4.8 KiB
LICENSE
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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
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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_msa · commit 4de6786
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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