About Rosetta:

Rosetta is software suite for modeling macromolecular structures. Its functionalities include structure prediction, design, and remodeling of proteins and nucleic acids. See the Rosetta documentation for more information.

License information:

For non-commercial users (academic, non-profits, government) Rosetta is available for free under a license that can be found here.

A paid license is required for commercial users and can be acquired through the University of Washington's CoMotion office. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.

See our Licensing FAQ for more information.

Release Versions (Precompiled Binaries and Source)

Development Version (Build From Source)

Docker Image

About PyRosetta:

PyRosetta is a Python-based interface to the Rosetta molecular modeling suite. It enables the user to use Python scripting to build powerful, custom modeling algorithms. See the PyRosetta documentation for more information.

License information:

For non-commercial users (academic, non-profits, government) PyRosetta is available for free under a license that can be found here.

A paid license is required for commercial users and can be acquired through the University of Washington's CoMotion office. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.

Installation Guide

Docker Image

About Foldit Standalone:

Foldit is a revolutionary crowdsourcing computer game enabling you to contribute to scientific research. See the About Foldit page to learn more.

License information:

For non-commercial users (academic, non-profits, government) Foldit Standalone is available for free.

A paid license is required for commercial users and can be acquired through the University of Washington's CoMotion office. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.

Quickstart Guide

About RoseTTAFold:

RoseTTA Fold is a protein folding package that uses deep learning to quickly and accurately predict protein structures based on limited information.

License information:

For non-commercial users (academic, non-profits, government) RoseTTAFold is available for free under a license that can be found here.

A paid license is required for commercial users and can be acquired through the University of Washington's CoMotion office. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.

Build From Source

About DeepAb:

DeepAb is an antibody structure prediction using interpretable deep learning.

License information:

For non-commercial users (academic, non-profits, government) DeepAb is available for free under a license that can be found here.

A paid license is required for commercial users and can be acquired through the University of Washington's CoMotion office. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.

Installation Guide

About MaSIF:

Molecular Surface Interaction Fingerprints (MaSIF) uses geometric deep learning to decipher patterns in molecular surfaces.

License information:

For non-commercial users (academic, non-profits, government) MaSIF is available for free under a license that can be found here.

A paid license is required for commercial users and can be acquired through the University of Washington's CoMotion office. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.

Installation Guide

About trRosetta2:

trRosetta2 is a deep learning method for protein folding prediction used by the Baker group in CASP14.

License information:

For non-commercial users (academic, non-profits, government) trRosetta2 is available for free under a license that can be found here.

A paid license is required for commercial users and can be acquired through the University of Washington's CoMotion office. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.

Build From Source

About Protein-Seq-Des:

Protein-Seq-Des is a tool for protein sequence design from a learned potential.

License information:

For non-commercial users (academic, non-profits, government) Protein-Seq-Des is available for free under a license that can be found here.

A paid license is required for commercial users and can be acquired through the University of Washington's CoMotion office. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.

Installation Guide

About FvHallucinator:

FvHallucinator is a tool that designs sequences that fold into a desired Fv structure using DeepAb (see above).

License information:

For non-commercial users (academic, non-profits, government) FvHallucinator is available for free under a license that can be found here.

A paid license is required for commercial users and can be acquired through the University of Washington's CoMotion office. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.

Installation Guide