How do I install the Protenix skill?

Run npx skillmds@latest add adaptyvbio/protenix in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Protenix safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Protenix?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Protenix free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Protenix?

adaptyvbio (@adaptyvbio) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Protenix by adaptyvbio — Agent Skill

Name: Protenix
Rating: 4.2 (5 reviews)
Author: adaptyvbio

Protenix

Structure prediction with Protenix, an open AlphaFold3 reproduction. Use this skill when: (1) Predicting complex structures with an AF3-class model, (2) Wanting an open alternative to AF3 alongside Boltz and Chai, (3) Validating designed binder-target complexes. For QC thresholds, use protein-qc. For ipSAE ranking, use ipsae.

adaptyvbio Updated Sep 10, 2026

File contents

Protenix Structure Prediction

Protenix is ByteDance's open PyTorch reproduction of AlphaFold3 (Apache 2.0). It is an AF3-class complex predictor, useful next to boltz and chai for cross-checking designed complexes. Runnable through biomodals.

Use Protenix-v2 for antibody-antigen complexes. The v2 model (464M params, April 2026) adds 9 to 13 percentage points of antibody-antigen accuracy over v1 at the DockQ > 0.23 threshold and is more sample-efficient (v2 at 5 seeds exceeds v1 at 1000). Select it with --model-name protenix-v2. For general complexes, the v1 base model is fine.

Prerequisites

Requirement	Value
Runner	Modal (biomodals)
GPU	L40S (default; `GPU` env var)
Setup	See Getting started

How to run

git clone https://github.com/hgbrian/biomodals && cd biomodals

printf '>protein|A\nMAWTPLLLLLLSHCTGSLSQ...\n' > target.faa

uv run --with modal modal run modal_protenix.py \
  --input-faa target.faa \
  --seeds 42 \
  --no-use-msa

Key parameters

Parameter	Default	Description
`--input-faa`	one required	FASTA input (or `--input-json`)
`--seeds`	`42`	Comma-separated seeds
`--use-msa` / `--no-use-msa`	MSA on	Pass `--no-use-msa` for single-sequence
`--model-name`	v1 base	Set `protenix-v2` for antibody-antigen complexes
`--use-mini`	off	Switch to the smaller `protenix_mini` model
`--out-dir`	`./out/protenix`	Output directory

When to use Protenix vs Boltz vs Chai

Need	Tool
Affinity head (small molecules)	boltz (Boltz-2)
Fastest, ligand support	chai
Open AF3 reproduction	protenix (v1 base)
Antibody-antigen complexes	protenix-v2

Ranking a shortlist across more than one predictor is more reliable than trusting a single model.

Troubleshooting

Issue	Cause	Fix
Missing input error	No `--input-faa`/`--input-json`	Provide one
Slow run	MSA enabled	Add `--no-use-msa`
OOM	Large complex	Use `--use-mini` or a larger GPU

Next: Rank with ipsae, filter with protein-qc.

---name: protenixdescription: Structure prediction with Protenix, an open AlphaFold3 reproduction. Use this skill when: (1) Predicting complex structures with an AF3-class model, (2) Wanting an open alternative to AF3 alongside Boltz and Chai, (3) Validating designed binder-target complexes. For QC thresholds, use protein-qc. For ipSAE ranking, use ipsae.license: MIT---# Protenix Structure Prediction[Protenix](https://github.com/bytedance/Protenix) is ByteDance's open PyTorchreproduction of AlphaFold3 (Apache 2.0). It is an AF3-class complex predictor, usefulnext to `boltz` and `chai` for cross-checking designed complexes. Runnable throughbiomodals.**Use Protenix-v2 for antibody-antigen complexes.** The v2 model (464M params, April2026) adds 9 to 13 percentage points of antibody-antigen accuracy over v1 at theDockQ > 0.23 threshold and is more sample-efficient (v2 at 5 seeds exceeds v1 at 1000).Select it with `--model-name protenix-v2`. For general complexes, the v1 base model isfine.## Prerequisites| Requirement | Value ||-------------|-------|| Runner | Modal (biomodals) || GPU | L40S (default; `GPU` env var) || Setup | See [Getting started](../../docs/getting-started.md) |## How to run```bashgit clone https://github.com/hgbrian/biomodals && cd biomodalsprintf '>protein|A\nMAWTPLLLLLLSHCTGSLSQ...\n' > target.faauv run --with modal modal run modal_protenix.py \ --input-faa target.faa \ --seeds 42 \ --no-use-msa```## Key parameters| Parameter | Default | Description ||-----------|---------|-------------|| `--input-faa` | one required | FASTA input (or `--input-json`) || `--seeds` | `42` | Comma-separated seeds || `--use-msa` / `--no-use-msa` | MSA on | Pass `--no-use-msa` for single-sequence || `--model-name` | v1 base | Set `protenix-v2` for antibody-antigen complexes || `--use-mini` | off | Switch to the smaller `protenix_mini` model || `--out-dir` | `./out/protenix` | Output directory |## When to use Protenix vs Boltz vs Chai| Need | Tool ||------|------|| Affinity head (small molecules) | boltz (Boltz-2) || Fastest, ligand support | chai || Open AF3 reproduction | protenix (v1 base) || Antibody-antigen complexes | protenix-v2 |Ranking a shortlist across more than one predictor is more reliable than trusting asingle model.## Troubleshooting| Issue | Cause | Fix ||-------|-------|-----|| Missing input error | No `--input-faa`/`--input-json` | Provide one || Slow run | MSA enabled | Add `--no-use-msa` || OOM | Large complex | Use `--use-mini` or a larger GPU |---**Next**: Rank with `ipsae`, filter with `protein-qc`.

Frequently asked questions