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.