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Agent skill

Alphafold2 Multimer

by BioTender-max in BioTender-max/awesome-bio-agent-skills

AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring.

MITAuto-check passedResearch & Science

Install Alphafold2 Multimer

skills CLI
$ npx skills add BioTender-max/awesome-bio-agent-skills --skill alphafold2-multimer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install BioTender-max/awesome-bio-agent-skills alphafold2-multimer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/BioTender-max/awesome-bio-agent-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/bioclaw_hub/alphafold2-multimer .claude/skills/alphafold2-multimer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
alphafold2-multimer
GitHub stars
197
Token cost
~1.4k tokens
SKILL.md length
336 words
Files
3 (incl. references)
Skills in repo
20
Repo updated
First seen
Licence
MIT

At a glance

AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring.

  • Validating designed sequences fold correctly
  • SKILL.md covers Prerequisites, How to run, Key parameters and Output format, plus 8 more sections
  • Calls modal, python and git; reaches github.com
  • Predicting binder-target complex structures

What it does

Alphafold2 Multimer is an agent skill from BioTender-max/awesome-bio-agent-skills. AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.

Its SKILL.md is about 1.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `README.md` and `references/multimer.md`).

It sits in Research & Science, covering Protein structure and design. It works with AlphaFold. The repository describes itself as: A curated collection of AI agent skills for biomedical research, covering genomics, proteomics, single-cell analysis, clinical AI, and protein design. The licence is MIT.

When your agent uses it

  • Validating designed sequences fold correctly
  • Predicting binder-target complex structures
  • Calculating confidence metrics (pLDDT
  • Self-consistency validation of designs

Example prompts

  • “/alphafold2-multimer”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 8cbdd18. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • modal
    • python
    • git

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • github.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Alphafold2 Multimer loads about 1.4k tokens when it runs, and up to ~2.3k if it reads all its reference files. Until then it costs about 124 tokens; SKILL.md has 336 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~124
When it runs · the whole SKILL.md, loaded when a task matches
~1.4k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~2.3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from BioTender-max/awesome-bio-agent-skills at commit 8cbdd18, republished under its MIT licence (© BioTender-max). 336 words, ~1,389 tokens.

Download SKILL.mdSave it as .claude/skills/alphafold2-multimer/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
alphafold2-multimer
description
AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Use this skill when: (1) Validating designed sequences fold correctly, (2) Predicting binder-target complex structures, (3) Calculating confidence metrics (pLDDT, pTM, ipTM), (4) Self-consistency validation of designs, (5) Multi-chain complex prediction with AlphaFold-Multimer. For faster single-chain prediction, use esm2-sequence-scoring. For QC thresholds, use protein-design-qc.
license
MIT
category
design-tools
tags
structure-prediction, validation, reference
biomodals_script
modal_alphafold.py

AlphaFold2 / AlphaFold-Multimer Validation

Plain-language role: Use AlphaFold when you want a reference-grade structure prediction check for a designed sequence or complex.

Prerequisites

RequirementMinimumRecommended
Python3.8+3.10
CUDA11.0+12.0+
GPU VRAM32GB40GB (A100)
RAM32GB64GB
Disk100GB500GB (for databases)

How to run

First time? See Installation Guide to set up Modal and biomodals.

bash
cd biomodals
modal run modal_colabfold.py \
  --input-faa sequences.fasta \
  --out-dir output/

GPU: A100 (40GB) | Timeout: 3600s default

Option 2: Local installation
bash
git clone https://github.com/deepmind/alphafold2-multimer.git
cd alphafold2-multimer

python run_alphafold.py \
  --fasta_paths=query.fasta \
  --output_dir=output/ \
  --model_preset=monomer \
  --max_template_date=2026-01-01
Option 3: ESMFold (fast single-chain)
bash
modal run modal_esmfold.py \
  --sequence "MKTAYIAKQRQISFVK..."

Key parameters

ParameterDefaultOptionsDescription
--model_presetmonomermonomer/multimerModel type
--num_recycle31-20Recycling iterations
--max_template_date-YYYY-MM-DDTemplate cutoff
--use_templatesTrueTrue/FalseUse template search

Output format

output/
├── ranked_0.pdb           # Best model
├── ranked_1.pdb           # Second best
├── ranking_debug.json     # Confidence scores
├── result_model_1.pkl     # Full results
├── msas/                  # MSA files
└── features.pkl           # Input features
Extracting metrics
python
import pickle

with open('result_model_1.pkl', 'rb') as f:
    result = pickle.load(f)

plddt = result['plddt']
ptm = result['ptm']
iptm = result.get('iptm', None)  # Multimer only
pae = result['predicted_aligned_error']

Sample output

Successful run
$ python run_alphafold.py --fasta_paths complex.fasta --model_preset multimer
[INFO] Running MSA search...
[INFO] Running model 1/5...
[INFO] Running model 5/5...
[INFO] Relaxing structures...

Results:
  ranked_0.pdb:
    pLDDT: 87.3 (mean)
    pTM: 0.78
    ipTM: 0.62
    PAE (interface): 8.5

Saved to output/

What good output looks like:

  • pLDDT: > 85 (mean, on 0-100 scale) or > 0.85 (normalized)
  • pTM: > 0.70
  • ipTM: > 0.50 for complexes
  • PAE_interface: < 10

Decision tree

Should I use AlphaFold?
│
├─ What are you predicting?
│  ├─ Single protein → ESMFold (faster)
│  ├─ Protein-protein complex → AlphaFold/ColabFold ✓
│  ├─ Protein + ligand → Chai or Boltz
│  └─ Batch of sequences → ColabFold ✓
│
├─ What do you need?
│  ├─ Highest accuracy → AlphaFold/ColabFold ✓
│  ├─ Fast screening → ESMFold
│  └─ MSA-free prediction → Chai or ESMFold
│
└─ Which AF2 option?
   ├─ Local installation → Full control, slow setup
   ├─ ColabFold → Easier, MSA server
   └─ Modal → Recommended for batch

Typical performance

Campaign SizeTime (A100)Cost (Modal)Notes
100 complexes1-2h~$8With MSA server
500 complexes5-10h~$40Standard campaign
1000 complexes10-20h~$80Large campaign

Per-complex: ~30-60s with MSA server.

Verify

bash
find output -name "ranked_0.pdb" | wc -l  # Should match input count

Troubleshooting

Low pLDDT regions: May indicate disorder or poor design Low ipTM: Interface not confident, check hotspots High PAE off-diagonal: Chains may not interact OOM errors: Use ColabFold with MSA server instead

Error interpretation
ErrorCauseFix
RuntimeError: CUDA out of memorySequence too longUse A100 or split prediction
KeyError: 'iptm'Running monomer on complexUse multimer preset
FileNotFoundError: databaseMissing MSA databasesUse ColabFold MSA server
TimeoutErrorMSA search slowReduce num_recycles

Next: protein-design-qc for filtering and ranking.

Inputs

  • One or more protein sequences in FASTA format, optionally grouped as a complex.
  • Optional template structures, MSA settings, and recycle count overrides.
  • A prediction workspace with enough disk for intermediate features and outputs.

Outputs

  • Predicted structure files such as PDB/mmCIF plus per-model confidence JSON or PKL files.
  • Model-level confidence metrics including pLDDT, pTM, ipTM, and PAE matrices.
  • A ranked prediction set ready for protein-design-qc filtering or ipsae ranking.

Next Step

Run protein-design-qc to filter low-confidence models, then use ipsae when ranking binders for experiments.

© BioTender-max, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skills/bioclaw_hub/alphafold2-multimer of BioTender-max/awesome-bio-agent-skills.

  • SKILL.md
  • README.md
  • references/multimer.md

Open the folder on GitHubat commit 8cbdd18

Compare with similar skills

Alphafold2 Multimer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Alphafold2 Multimer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Alphafold2 Multimer this skillBioTender-max/awesome-bio-agent-skills197—~1.4kAutomated safety check: PassMIT
Alphafold Database Fetch And Analyzegoogle-deepmind/science-skills3.2k2 repos~1.2kAutomated safety check: PassApache-2.0
Alphafoldadaptyvbio/protein-design-skills1634 repos~1.2kAutomated safety check: PassMIT
Chaiadaptyvbio/protein-design-skills1634 repos~1.5kAutomated safety check: PassMIT
Biopipelineslocbp-uzh/biopipelines109—~2.4kAutomated safety check: PassMIT
Rfdiffusionadaptyvbio/protein-design-skills1634 repos~2.3kAutomated safety check: PassMIT

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Works with

Categories

Questions about Alphafold2 Multimer

What does Alphafold2 Multimer do?

AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring. Alphafold2 Multimer is an agent skill from BioTender-max/awesome-bio-agent-skills. AlphaFold2 / AlphaFold-Multimer structure prediction for validation and confidence scoring.

When should I use Alphafold2 Multimer?

Alphafold2 Multimer fits situations like: validating designed sequences fold correctly; predicting binder-target complex structures; calculating confidence metrics (pLDDT; self-consistency validation of designs.

How do I install Alphafold2 Multimer in Claude Code?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill alphafold2-multimer -a claude-code`. Or copy the skill folder (skills/bioclaw_hub/alphafold2-multimer in BioTender-max/awesome-bio-agent-skills) into .claude/skills/alphafold2-multimer in your project. Claude Code loads it when a task matches its description.

How do I install Alphafold2 Multimer in Codex?

Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill alphafold2-multimer -a codex`. Or copy the skill folder (skills/bioclaw_hub/alphafold2-multimer in BioTender-max/awesome-bio-agent-skills) into .agents/skills/alphafold2-multimer in your project. Codex loads it when a task matches its description.

Can I use Alphafold2 Multimer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add BioTender-max/awesome-bio-agent-skills --skill alphafold2-multimer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/alphafold2-multimer, .gemini/skills/alphafold2-multimer, .github/skills/alphafold2-multimer and .opencode/skills/alphafold2-multimer in your project.

What does Alphafold2 Multimer need to run?

Going by SKILL.md and its folder, Alphafold2 Multimer needs the command-line tools its instructions call (modal, python and git). Our summary lists: Python 3.

Does Alphafold2 Multimer access the network?

SKILL.md names 1 domain. In commands or code: github.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Alphafold2 Multimer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Alphafold2 Multimer use?

Alphafold2 Multimer is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Alphafold2 Multimer use?

About 1.4k tokens (SKILL.md is roughly 5.6k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 887 tokens, read only when the agent opens those files.

What are the alternatives to Alphafold2 Multimer?

Skills that share tags, products or a category with Alphafold2 Multimer: Alphafold Database Fetch And Analyze (google-deepmind/science-skills, 3.2k stars), Alphafold (adaptyvbio/protein-design-skills, 163 stars), Chai (adaptyvbio/protein-design-skills, 163 stars) and Biopipelines (locbp-uzh/biopipelines, 109 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Alphafold2 Multimer?

BioTender-max (a GitHub user) maintains it in BioTender-max/awesome-bio-agent-skills, which has 197 GitHub stars. The repository holds 20 skills in this directory. The repository was last updated on July 1, 2026.

Source: BioTender-max/awesome-bio-agent-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.