Machine learning methods for protein structure prediction have taken advantage of the evolutionary information present in multiple sequence alignments to derive accurate structural information, but predicting structure accurately from a single sequence is much more difficult. Lin et al. trained transformer protein language models with up to 15 billion parameters on experimental and high-quality predicted structures and found that information about atomic-level structure emerged in the model as it was scaled up. They created ESMFold, a sequence-to-structure predictor that is nearly as accurate as alignment-based methods and considerably faster. The increased speed permitted the generation of a database, the ESM Metagenomic Atlas, containing more than 600 million metagenomic proteins

Today we're announcing ESMFold2, an open scientific engine to power prediction, design, and discovery across protein biology.

The new model delivers state of the art performance on protein interactions, especially antibodies, a critical modality for therapeutics.

We have designed and validated miniprotein binders and single chain antibodies across five therapeutic targets that are important in cancer and immunology. We are seeing very high success rates, and affinities at levels consistent with therapeutic activity.

We’re also releasing an atlas of 6.8 billion proteins, and 1.1 billion predicted structures.

ESMFold2 is built on a state of the art language model that has been trained on billions of protein sequences.