RoseTTAFold
A deep learning system for accurate protein structure and interaction prediction using a three-track neural network.
What is RoseTTAFold?
RoseTTAFold is a deep learning system that predicts protein 3D structures and protein-protein interactions from amino acid sequences. It uses a three-track neural network to integrate sequence, distance, and coordinate information, solving the critical problem of determining protein structure computationally. The system enables researchers to model biological macromolecules without relying solely on experimental methods like X-ray crystallography.
Computational biologists, bioinformaticians, and structural biology researchers who need to predict protein structures or analyze protein interactions for drug discovery, protein engineering, or basic research.
Developers choose RoseTTAFold for its state-of-the-art accuracy in protein structure prediction, its ability to model both monomers and complexes, and its open-source implementation that allows for local deployment and customization unlike cloud-only alternatives.
Overview
This package contains deep learning models and related scripts for RoseTTAFold
Use Cases
Best For
- Predicting 3D structures of proteins from amino acid sequences
- Modeling protein-protein interactions and complexes
- Screening for potential protein-protein interactions at scale
- Computational biology research and method development
- Drug discovery and target identification workflows
- Educational use in structural bioinformatics courses
Not Ideal For
- Commercial applications requiring unrestricted use of trained weights
- Environments with limited storage or bandwidth for large databases (over 400GB)
- Projects needing quick, one-off predictions without extensive setup
- Teams without expertise in bioinformatics or deep learning system administration
Pros & Cons
Pros
Combines 1D sequence, 2D distance, and 3D coordinate data for robust predictions, as described in the README for accurate protein structure modeling.
Supports both single protein structures and protein-protein interactions, with specific scripts like predict_complex.py for complexes, enhancing versatility in biological research.
Offers end-to-end neural network and PyRosetta-based physics refinement versions, allowing users to choose between speed and accuracy based on their needs.
Provides predicted CA-RMSD or residue-wise CA-lddt scores in output PDB files, helping researchers assess prediction reliability without extra tools.
Cons
Requires downloading over 400GB of databases, setting up multiple conda environments, and installing third-party software, making it time-consuming and error-prone.
Trained weights are only available for non-commercial use under the Rosetta-DL license, limiting applications in commercial drug discovery or biotech.
README mentions segmentation faults with hhsuite and suggests compiling from source, indicating potential compatibility and reliability problems in deployment.
Frequently Asked Questions
Related Projects
AlphaFold3AlphaFold 3 inference pipeline.
Evolutionary Scale Modeling (ESM)Evolutionary Scale Modeling (esm): Pretrained language models for proteins
Boltz-1Official repository for the Boltz biomolecular interaction models
OpenFoldTrainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
Found a gem we're missing?
Open-Awesome is built by the community, for the community. Submit a project, suggest an awesome list, or help improve the catalog on GitHub.