OpenFold3 Training Data

Description

This dataset contains MSAs and predicted structures for 13 million long (sequence length >= 200 amino acids) monomers from the MGNIFY database. These MSAs were generated using the AF3 protocol, and were used to predict structures with AlphaFold2. This data serves as the long monomer distillation set for Openfold3, an open-source, all-atom ligand, RNA and protein structure prediction software.

Update Frequency

Never

License

CC BY 4.0

Documentation

https://github.com/aqlaboratory/openfold3-training-data-RODA/tree/main

Managed By

OpenFold

See all datasets managed by OpenFold.

Contact

https://github.com/aqlaboratory/openfold3-training-data-RODA/issues

How to Cite

OpenFold3 Training Data was accessed on DATE from https://registry.opendata.aws/openfold3. Additionally, please cite our prior manuscript.

Usage Examples

Tutorials

• Deploying OpenFold3 with NVIDIA NIMs on Brev & AWS EC2 by Glòria Macià
  

Publications

• OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization by Ahdritz, Gustaf; Bouatta, Nazim; Kadyan, Sachin; Xia, Qinghui; Gerecke, William; O'Donnell, Timothy J, et al
  
• OpenProteinSet: Training data for structural biology at scale by Ahdritz, Gustaf; Bouatta, Nazim; Kadyan, Sachin; Jarosch, Lukas; Berenberg, Daniel; Fisk, Ian, et al