ProteinMPNN
Designs amino-acid sequences that fold to a given backbone.
Developer: Justas Dauparas, Institute for Protein Design, University of Washington
Overview
- Developer
- Justas Dauparas, Institute for Protein Design, University of Washington
- Commercial rights
- Commercial OK
- Commercial cost
- Free commercially
- Compute
- CPU
- Open weights
- Yes
- Last verified
Capabilities
Inverse folding. Input: a backbone PDB (optionally with fixed positions, chain bias, symmetry); output: amino-acid sequences predicted to fold to that backbone, with per-residue probabilities and scores. CPU-fast; the standard step after any backbone generator.
When to use it
The standard inverse-folding step after any backbone generator; runs fine on CPU. It optimises foldability, not binding or developability, so you still need structure prediction and liability filtering afterwards.
Input / Output
- Input
- a backbone PDB (optionally with fixed positions, chain bias, symmetry)
- Output
- amino-acid sequences predicted to fold to that backbone, with per-residue probabilities and scores
Compute
Commercial Licensing
Commercial rights — may a company use it?
Commercial use OKLicence as writtenMIT License
MIT LICENSE file in the repo; no field-of-use restriction.
The licence read at the source allows use by a company. Attribution or copyleft conditions may still apply — read the note.
Commercial cost — is commercial use free of charge?
Free for commercial useNothing to pay: the licence read at the source grants commercial use with no fee, and the code and weights are public downloads. You still pay for your own compute.
Price and rights are separate: this panel is about money, the panel above is about permission.
- Commercial OK ≠ Free of charge. A tool can be licensed for company use and still need a paid licence for a dependency (BindCraft → PyRosetta).
- Free download ≠ Commercially licensed. Weights you can download at no cost may still forbid commercial use (AlphaFold 3).
- Unverified ≠ Commercially permitted. If no published statement settles it, treat it as unknown — not as a yes.
- Every component counts. Each status considers the code licence, the model-weights terms, required dependencies and any terms of use.
Model Weights
Open weights. A separate weights licence is Not documented as its own field; where weights carry different terms from the code, that is stated in the licence explanation above.
Dependencies
Not documented as a separate field. Any dependency that affects commercial use (for example a paid PyRosetta licence) is named in the licence and cost explanations above.
Official References
- Official site / repo
- github.com/dauparas/ProteinMPNN
- Licence sources
From the makers of FoldScout
From AI Design to Wet-Lab Validation
Designed a promising protein or antibody? Seq2Study helps translate AI-generated sequences into experimental data.
- Protein Expression
- Protein Purification
- Binding Affinity
- Aggregation Assessment
- Stability Evaluation
- Developability Assessment
Get told when a licence changes
Licences on these tools do change. One short email when a status flips or a tool is added. No other use of your address.