ProteinMPNN
No estimate
No hardware requirements for this model
This model's weights are open, but no parameter count has been published for it. Every memory and speed figure starts from that number, so we would rather show nothing than a fabricated estimate.
On record
Full specification
Everything on record for this model. Most of it describes how it was trained rather than how it runs — useful context for judging how much work went into it, and how it compares with models built at a different scale.
Origin
Who built this model, where, and when it was published.
- Organisation
- University of Washington,Wageningen University and Research,NERSC, Lawrence Berkeley National Laboratory
- Organisation type
- Academia,Academia,Government
- Country
- United States of America, Netherlands
- Published
- 15 September 2022
- Authors
- J. Dauparas, I. Anishchenko, N. Bennett, H. Bai, R. J. Ragotte, L. F. Milles, B. I. M. Wicky, A. Courbet, R. J. de Haas, N. Bethel, P. J. Y. Leung, T. F. Huddy, S. Pellock, D. Tischer, F. Chan, B. Koepnick, H. Nguyen, A. Kang, B. Sankaran, A. K. Bera, N. P. King, D. Baker
What it does
The problem areas the model was built for. A model can carry several of each.
- Domain
- Biology
- Task
- Protein or nucleotide language model (pLM/nLM), Protein design
Size
How large the model is and how much data it was trained on. Parameters are the figure that decides whether it fits on a given graphics card.
- Training data
- tokens
19,700 proteins * 0.8 [train split] × 300 residues/protein [assumtion] = 4 728 000 residues ≈ 5.0 × 10⁶ datapoints
Availability
Whether you can obtain the model and run it on your own hardware, which is what decides if any of the graphics-card figures on this page apply.
- Weights
- Open — downloadable
- Model access
- Open weights (unrestricted)
- Training code
- Open source
MIT license https://github.com/dauparas/ProteinMPNN
How it is classified
Labels the source dataset applies when tracking notable models, and how confident it is in the entry.
- Record confidence
- Likely
- Citations
- 793
Sources
Where this record came from and when it was last checked.
- Reference
- Robust deep learning–based protein sequence design using ProteinMPNN
- Last updated
- 28 November 2025
What the numbers mean
What this model is
ProteinMPNN was published by University of Washington,Wageningen University and Research,NERSC, Lawrence Berkeley National Laboratory, in United States of America, in September 2022. It comes out of academia,Academia,Government.
It works in Biology, and is recorded as doing protein or nucleotide language model (pLM/nLM), Protein design.
The weights being open is what puts this page in the calculator rather than only in the catalogue: it is a model you can actually hold.
Answers
ProteinMPNN — common questions
How many parameters does ProteinMPNN have?
No parameter count has been published for ProteinMPNN, which is why no memory or speed figure appears on this page.
Who created ProteinMPNN?
ProteinMPNN was published by University of Washington,Wageningen University and Research,NERSC, Lawrence Berkeley National Laboratory, based in United States of America, categorised as academia,Academia,Government.
When was ProteinMPNN released?
ProteinMPNN was published in September 2022. Capability per parameter has improved considerably since, so a newer model of the same size is often the better use of the same hardware.
What is ProteinMPNN used for?
ProteinMPNN works in Biology, and is recorded as handling protein or nucleotide language model (pLM/nLM), Protein design. Models frequently carry more than one of each, and the tags describe purpose rather than capability limits.
Where can I download ProteinMPNN?
The weights for ProteinMPNN are published, though we do not hold a repository link for it. This site calculates hardware requirements rather than hosting model files.
What GPU do I need to run ProteinMPNN?
We cannot say. ProteinMPNN has open weights, but no parameter count has been published for it, and every memory and speed calculation starts from that number. We would rather show nothing than a fabricated estimate.
Is ProteinMPNN open source?
Its weights are published, so ProteinMPNN can be downloaded and run on your own hardware. Note that open weights is not the same as open source in the full sense — it says nothing about the training data, the training code, or the commercial terms attached.
The other direction
Looking at it from the other side?
This page starts from the model. If you already own a card and want to know everything it will run, start from the hardware instead.