The Dayhoff Atlas: scaling sequence diversity for improved protein generation

Modern biology is powered by the organization of biological information, a framework pioneered in 1965 by Margaret Dayhoff’s Atlas of Protein Sequence and Structure. Databases descended from this common ancestor power computational methods that have revolutionized our ability to understand and design proteins. We introduce the Dayhoff Atlas, a centralized collection of both protein sequence data and generative protein language models, as a modern-day resource for protein biology in the age of AI. We combined and reclustered metagenomic sequences with UniRef100 to create GigaRef, which includes 3.34 billion protein sequences across 1.70 billion clusters and provides the largest open dataset of natural proteins to date. To fuse the richness of protein structure with the scalability of sequence space, we generated structure-based synthetic data, producing BackboneRef, a first-in-class dataset of 46 million synthetic protein sequences predicted from 240,811 de novo designed backbones. Using these data and evolutionary sequence alignments, we trained the Dayhoff family of protein language models, including the first model that combines single proteins and sets of evolutionarily-related sequences at scale. Operating only in amino acid sequence space, the Dayhoff models can natively predict mutation effects on fitness, scaffold structural motifs, and perform guided generation of new proteins within a specific family. Learning from metagenomic and structure-based synthetic data from the Dayhoff Atlas increased the cellular expression rates of generated proteins, highlighting the real-world value of expanding the scale, diversity, and novelty of protein sequence data. We release the Dayhoff Atlas, including all datasets, code, and models, under a permissive license to continue to bring the power of computation to protein biology and design.