Retrieve and analyze AlphaFold predicted structures for a protein. Use when the user provides a specific UniProt Accession ID and wants structural confidence metrics (pLDDT), domain boundary analysis, or disorder assessment. Do not use if the user only has a protein name, gene name, or amino acid sequence — ask for a UniProt ID first.
uv: Read the uv skill and follow its Setup instructions to ensure
uv is installed and on PATH.
User Notification: If
.licenses/alphafold_database_fetch_and_analyze_LICENSE.txt does not already
exist in the workspace root directory then (1) prominently notify the user
to check the terms at https://alphafold.ebi.ac.uk/, then (2) create the file
recording the notification text and timestamp.
Overview
Downloads AlphaFold predicted structures (mmCIF) and Predicted Aligned Error
(PAE) matrices from the AlphaFold Database for a given UniProt ID, then performs
automated heuristic analysis on structural confidence (pLDDT), intrinsically
disordered regions, rigid domain boundaries, and inter-domain flexibility.
Do NOT use when:
The user only has a protein name, gene name, or amino acid sequence (no
UniProt ID) — ask them to look up the ID on
UniProt.
The user wants to search for structural homologs (use Foldseek).
The user wants to run AlphaFold predictions on a custom sequence.
The user needs experimental PDB structures (use RCSB PDB).
Core Rules
Use the Wrapper: ALWAYS execute the provided helper scripts to query the
database rather than accessing the database directly. The scripts
automatically enforce the required rate limit gracefully.
Do not attempt to calculate domain boundaries or assess structural disorder
yourself; always rely on the output provided by the script.
If this skill is used, ensure this is mentioned in the output.
Utility Scripts
1. Fetch Structure Files
Downloads the .cif structure file, _predicted_aligned_error.json, and API
metadata JSON (-metadata.json) for a UniProt ID. Handles fragment fallback for
very large proteins.
Examples:
uv run scripts/fetch_structure.py P00520 -o /path/to/output/
uv run scripts/fetch_structure.py P04637 -o /path/to/custom_results/
Always specify -o with an absolute path or a path relative to the user's
project root, never a path relative to the skill directory.
2. Analyze pLDDT Confidence
Reads pLDDT confidence metrics from a saved AFDB metadata JSON file (produced by
fetch_structure.py) and prints a heuristic confidence assessment (structured,
disordered, mixed).
Example:
uv run scripts/analyze_plddt.py ./data/AF-P00520-F1-metadata.json
3. Analyze PAE / Domain Boundaries
Reads a downloaded PAE JSON file and detects rigid domain boundaries using a
sliding-window PAE heuristic.
Example:
uv run scripts/analyze_pae.py ./data/AF-P00520-F1-predicted_aligned_error_v6.json
Interpreting the Output
The script prints analysis to stdout. Read it carefully and synthesize the
results for the user:
Isoform / Large Protein Warning (MANDATORY): Check the script output for
any [!] WARNING lines. If the script reports that no canonical entry was
found and an isoform was used, or if the protein is very large (>2700 AAs),
you MUST prominently relay this warning to the user. Do not omit this
warning.
Synthesize the Structural Analysis: Combine the "pLDDT Conclusion" and
the "PAE Structural Conclusion" into a single, cohesive overall summary.
Describe the protein's overall folding confidence, the presence of
disordered regions, and its rigid domain layout.
Highlight the supporting metrics:
Overall Global pLDDT and the breakdown of fraction confidence
(especially Very Low vs. Very High).
Domain Boundary Analysis (number of distinct global domains and their
specific residue ranges).
Explicit Disorder Warning: If the analysis concludes that the protein is
highly intrinsically disordered (e.g., high fraction of <50 pLDDT or lack of
rigid domains), issue a separate, prominent warning. Advise the user against
proceeding with whole-protein downstream structural analysis (like Foldseek
or docking). If small ordered domains exist amidst the disorder, advise the
user to restrict any future analysis strictly to those specific residue
boundaries.
Remind the user that per-residue pLDDT is embedded in the B-factor column of
the downloaded mmCIF file.
We use the uv package manager to handle dependencies. The first time you
trigger a Science skill, the agent will ask for approval to install uv, and
then proceed to respond to your scientific query / task. We recommend restarting
Antigravity after this first time installation.
Some skills, such as AlphaGenome, require an API key to function. Others, such
as ClinVar and OpenAlex, benefit from an API key to unlock higher rate limits
but are still functional without one. The agent should prompt you to obtain the
API key and guide you through writing in the correct location. However, if you
would rather do this yourself, you can run a command like this in your terminal:
echo "ALPHAGENOME_API_KEY=your_actual_api_key" >> ~/.env
Customizing or creating skills
If you want to customize an existing Science skill or create a new skill of your
own, you should not modify the files inside the Antigravity Science plugin
installation directory, as your changes will be overwritten whenever the plugin
is updated. Instead, place your custom or modified skills elsewhere, e.g. in
your personal skills directory:
All software is licensed under the Apache License, Version 2.0 (Apache 2.0); you
may not use this file except in compliance with the Apache 2.0 license. You may
obtain a copy of the Apache 2.0 license at:
https://www.apache.org/licenses/LICENSE-2.0
As set out in the attached file
‘Skill Licences and Terms of Use’ certain third party data
sources referenced within individual Skill files have their own applicable
licenses and/or terms of use. See the
‘Skill Licences and Terms of Use’ file for more
information. You are responsible for ensuring that your use of individual Skill
files complies with any such applicable licenses/ terms of use.
Unless required by applicable law or agreed to in writing, all software and
materials distributed here under the Apache 2.0 or CC-BY licenses are
distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,
either express or implied. See the licenses for the specific language governing
permissions and limitations under those licenses.
