alphafold2 skillA
alphafold2 is agent-read markdown (skill) from pku-yuangroup/openai4s: Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer evoformer, to validate designed sequences by self-consistency pLDDT, ipTM, and RMSD, or to run a quick MSA-backed prediction using the public MMseqs2 server..
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
# AlphaFold2 (ColabFold runner) This skill wraps AlphaFold2 and AlphaFold2-Multimer through `colabfold_batch`, which replaces DeepMind's local-database MSA pipeline with a call to the public MMseqs2 server — so a prediction is one command and one FASTA, not a 2 TB database mount. AF2 remains the reference monomer predictor and the multimer model is still a strong protein–protein validator, but it does not handle ligands or nucleic acids; for those, route to `boltz`, `chai1`, or `openfold3`. The ColabFold code is MIT (github.com/sokrypton/ColabFold) and the AlphaFold2 code is Apache-2.0 (github.com/google-deepmind/alphafold); the AF2 model parameters are CC-BY-4.0 with DeepMind's terms of use. ## Running it ```bash colabfold_batch input.fasta out \ --num-recycle 3 \ --model-type alphafold2_multimer_v3 ``` The input is a plain FASTA. For a complex, put every chain on one sequence line separated by `:` — `colabfold_batch` builds a paired MSA per segment and runs the multimer model when it sees the colon (so the explicit `--model-type alphafold2_multimer_v3` above is belt-and-braces). For monomers omit `--model-type` and the colon. `--templates` and `--amber` add PDB templates …
Read the whole file at its exact version.
How to install
mdr add pku-yuangroup/openai4s/alphafold2@git:20260828.e9753bemdr add pku-yuangroup/openai4s/alphafold2@sha256:137f4859c1ae250bPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_6553p46hp7zfq3gu)
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Versions
| version | committed | commit | size | audit | |
|---|---|---|---|---|---|
| git:20260828.e9753be latest | 2026-08-28 | e9753be | 5,062 B | A | view · diff |
| git:20260706.f35863d | 2026-07-06 | f35863d | 4,998 B | A | view |
Audit of the latest version
- pass: Frontmatter block present
- pass: Frontmatter declares a name
- pass: Frontmatter declares a description
- pass: Size between 200 bytes and 200 KB (5062 bytes)
- pass: No zero-width or bidi control characters
- pass: No instruction hidden inside an HTML comment
- pass: No link to an exfiltration or paste host
- pass: No credential-shaped string
- pass: No instruction to send local credentials anywhere
- pass: No text hidden with inline styles
- pass: No prompt-injection phrasing
- pass: No curl or wget piped into a shell
- pass: No recursive delete of root, home or parent
- pass: No instruction to read or print local credentials
- pass: No base64 blob over 200 characters
- pass: No link to a raw IP address
- pass: No script tag
Source
pku-yuangroup/openai4s · 586 stars · license MIT · pushed 2026-09-23 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_6553p46hp7zfq3gu GET https://markdownregistry.com/api/v1/resolve?ref=pku-yuangroup/openai4s/alphafold2 GET https://markdownregistry.com/api/v1/blob/137f4859c1ae250b5c8150f69eec96ec66e7c57ff5b314fad2688ad76eed0404
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