A.1 Access instructions OpenProteinSet is hosted by the Registry of Open Data on AWS (RODA) and can be accessed at the following link: registry.opendata.aws/openfold/. A.2 Documentation and intended uses We include a datasheet [1] in Section B. Detailed documentation on the precise structure and content of the dataset is provided on the dataset's landing page. A.3 Data format All OpenProteinSet files are in standard plaintext formats (A3M for MSAs, HHSearch format for template hits, and PDB for structure files) that can be read by a wide variety of bioinformatics software. A.5 License OpenProteinSet is made available under the CCBY 4.0 license. A copy of the license is provided with the dataset.

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.

In recent years, machine learning has demonstrated impressive capability in handling molecular science tasks. To support various molecular properties at scale, machine learning models are trained in the multi-task learning paradigm. Nevertheless, data of different molecular properties are often not aligned: some quantities, e.g.

RNA plays a pivotal role in translating genetic instructions into functional outcomes, underscoring its importance in biological processes and disease mechanisms.

Anillustrativeexample inFigure 1(a)showcases thatthecorrelations analysis among amino acids (AAs) sites could reveal contacts or conservative regions in the folding structure.

Machine learning applied to protein sequences is an increasingly popular areaof research.