OmegaFold
High-resolution de novo protein structure prediction from amino acid sequences using deep learning.
What is OmegaFold?
OmegaFold is a deep learning-based tool for predicting high-resolution 3D protein structures directly from their amino acid sequences. It solves the problem of structure prediction for novel proteins where traditional homology modeling fails, enabling researchers to understand protein function and interactions without experimental data. The model operates de novo, requiring only the primary sequence as input.
Computational biologists, bioinformaticians, and structural biology researchers who need to predict protein structures for novel sequences, orphan proteins, or engineered proteins without relying on evolutionary information.
OmegaFold provides accurate de novo structure prediction without requiring multiple sequence alignments, making it uniquely suited for novel proteins. Its optimized memory usage allows handling long sequences, and it offers configurable trade-offs between resource consumption and prediction quality.
Overview
OmegaFold Release Code
Use Cases
Best For
- Predicting structures for novel proteins with no known homologs
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