OpenProteinSet

Stream data with DDA:

from dagshub.streaming import DagsHubFilesystem

fs = DagsHubFilesystem(".", repo_url="https://dagshub.com/DagsHub-Datasets/openfold-dataset")

fs.listdir("s3://openfold")

Description:

Multiple sequence alignments (MSAs) for 132,000 unique Protein Data Bank (PDB) chains, covering 640,000 PDB chains in total, and 4,850,000 UniClust30 clusters. Template hits are also provided for the PDB chains and 270,000 UniClust30 clusters chosen for maximal diversity and MSA depth. MSAs were generated with HHBlits (-n3) and JackHMMER against MGnify, BFD, UniRef90, and UniClust30 while templates were identified from PDB70 with HHSearch, all according to procedures outlined in the supplement to the AlphaFold 2 Nature paper, Jumper et al. 2021. We expect the database to be broadly useful to structural biologists training or validating deep learning models for protein structure prediction and related tasks.

Contact:

Multiple sequence alignments (MSAs) for 132,000 unique Protein Data Bank (PDB) chains, covering 640,000 PDB chains in total, and 4,850,000 UniClust30 clusters. Template hits are also provided for the PDB chains and 270,000 UniClust30 clusters chosen for maximal diversity and MSA depth. MSAs were generated with HHBlits (-n3) and JackHMMER against MGnify, BFD, UniRef90, and UniClust30 while templates were identified from PDB70 with HHSearch, all according to procedures outlined in the supplement to the AlphaFold 2 Nature paper, Jumper et al. 2021. We expect the database to be broadly useful to structural biologists training or validating deep learning models for protein structure prediction and related tasks.

Update Frequency:

Never

Managed By:

OpenFold

Resources:

1. resource:
   • Description: A repository of MSAs and template hits.
   • ARN: arn:aws:s3:::openfold
   • Region: us-east-1
   • Type: S3 Bucket

Tags:

openfold, msa, protein, protein template, protein folding, alphafold, open source software, life sciences, aws-pds

Tutorials:

1. tutorial:
   • Title: Run inference at scale for OpenFold, a PyTorch-based protein folding ML model, using Amazon EKS
   • URL: https://aws.amazon.com/blogs/machine-learning/run-inference-at-scale-for-openfold-a-pytorch-based-protein-folding-ml-model-using-amazon-eks/
   • AuthorName: Shubha Kumbadakone, Ankur Srivastava, and Sachin Kadyan
   • Services: EKS, FSx

Publication:

1. publication:
   • Title: OpenFold: Retraining AlphaFold2 yields new insights into its learning mechanisms and capacity for generalization
   • URL: https://biorxiv.org/content/10.1101/2022.11.20.517210
   • AuthorName: Ahdritz, Gustaf; Bouatta, Nazim; Kadyan, Sachin; Xia, Qinghui; Gerecke, William; O'Donnell, Timothy J, et al