OpenProteinSet: Training data for structural biology at scale
Authors: name: Gustaf Ahdritz, name: Nazim Bouatta, name: Sachin Kadyan, name: Lukas Jarosch, name: Daniel Berenberg, name: Ian Fisk, name: Andrew M. Watkins, name: Stephen Ra, name: Richard Bonneau, name: Mohammed AlQuraishi

Multiple sequence alignments (MSAs) of proteins encode rich biological
information and have been workhorses in bioinformatic methods for tasks like
protein design and protein structure prediction for decades. Recent
breakthroughs like AlphaFold2 that use transformers to attend directly over
large quantities of raw MSAs have reaffirmed their importance. Generation of
MSAs is highly computationally intensive, however, and no datasets comparable
to those used to train AlphaFold2 have been made available to the research
community, hindering progress in machine learning for proteins. To remedy this
problem, we introduce OpenProteinSet, an open-source corpus of more than 16
million MSAs, associated structural homologs from the Protein Data Bank, and
AlphaFold2 protein structure predictions. We have previously demonstrated the
utility of OpenProteinSet by successfully retraining AlphaFold2 on it. We
expect OpenProteinSet to be broadly useful as training and validation data for
1) diverse tasks focused on protein structure, function, and design and 2)
large-scale multimodal machine learning research.