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
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