Dayhoff

Dataset Card for Dayhoff Dayhoff is an Atlas of both protein sequence data and generative language models — a centralized resource that brings together 3.34 billion protein sequences across 1.7 billion clusters of metagenomic and natural protein sequences (GigaRef), 46 million structure-derived synthetic sequences (BackboneRef), and 16 million multiple sequence alignments (OpenProteinSet). These models can natively predict zero-shot mutation effects on fitness, scaffold structural motifs by conditioning on evolutionary or structural context, and perform guided generation of novel proteins within specified families. 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. The Dayhoff model architecture combines state-space Mamba layers with Transformer self-attention, interleaved with Mixture-of-Experts modules to maximize capacity while preserving efficiency. It natively handles long contexts, allowing both single sequences and unrolled MSAs to be modeled. Trained with an autoregressive objective in both N→C and C→N directions, Dayhoff supports order-agnostic infilling and scales to billions of parameters. Dataset Structure The Dayhoff models were trained on the Dayhoff Atlas, with varying data mixes which include: **UniRef50** (**UR50**) - dataset from UniProt, clustered at 50% sequence identity, contains only cluster representatives. * _Splits: train (25 GB), test (26 MB), valid (26 MB)_ **UniRef90** (**UR90**) - dataset from UniProt, clustered at 90% sequence identity, contains cluster representatives and members. * _Splits: train (83 GB), test (90 MB), valid (87 MB)_ **GigaRef** (**GR**)– 3.34B protein sequences across 1.7B clusters of metagenomic and natural protein sequences. There are two subsets of gigaref: * **GigaRef-clusters** (**GR**) - Only includes cluster re