New protein-folding AI vastly expands AlphaFold’s efforts

New protein-folding AI predicts the structures of 1 billion proteins

Ewen Callaway, by Miriam Nadaff and nature magazine

The known protein universe has become very large. A newly released artificial-intelligence tool has generated an atlas of more than a billion predicted protein structures and billions more of protein sequences.

The database, known as the ESM Atlas, was unveiled today by researchers at the Chan Zuckerberg Initiative’s BioHub, a biomedical institute created in San Francisco, California by Facebook founder Mark Zuckerberg and his wife, physician and teacher Priscilla Chan.

atlas eclipses alphafold database of protein structures predicted by over 800 million entries, and a Previous ESM Atlas About 300 million.

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The predictions were made using ESMFold2, an AI model that Biohub says surpasses the performance of AlphaFold3, the latest version of Google DeepMind’s system and other protein-structure prediction AIs. The atlas is described in a preprint released today.

“This atlas shows the totality of protein biology and especially the parts that are most unknown,” says Alex Reeves, Biohub science lead who led the effort. “We think this is going to be a really powerful substrate for discovering new biology.”

Other scientists are impressed by the results, especially that ESMFold2 is completely open source. But the biohub model is entering an increasingly crowded field, with competing open-source and proprietary protein models profiting at a breakneck pace.

Antibody prediction

ESMFold2 is based on a ‘protein language’ model that Rives’ team unveiled in 2024, which was trained on billions of proteins from the tree of life. This includes ‘metagenomic’ sequences from soil, sea and other environments, which are absent from the AlphaFold database of predicted protein structures.

Rives’ team says ESMFold2 outperforms existing methods, including AlphaFold3, in determining the true structure of complexes of interacting proteins – including antibody molecules binding to their antigenic molecular targets.

In a preprint, the researchers describe how they used ESMFold2 to design new antibodies and other proteins that can bind tightly to proteins involved in cancer and immune-related conditions. When built and tested in the laboratory, a high proportion of the designs worked as predicted.

Rives’ team used the tool to create an atlas containing information on the sequences of 6.8 billion proteins, along with 1.1 billion predicted protein structures. Most of these come from metagenomic sequences that were only poorly characterized. Rives hopes that the atlas – which will be freely accessible – will help scientists make connections between known and unknown parts of the protein universe. Using ATLAS, researchers found structural similarities between CRISPR microbial defense proteins and gene-editing proteins identified in soil fungi in 2023 and gene-editing proteins found in other eukaryotic species.

Supplementary Database

Gemma Atkinson, a computational biologist at Lund University in Sweden, says the newly released atlas should be “an extraordinary resource for biology.” “It is exciting to see how large-scale protein language models can capture the fundamental laws of protein biology.”

Christine Orengo, a computational biologist at University College London, says the predictions, which will first need to be evaluated, could help uncover new protein folds and functions with implications for the basic understanding of protein design and biology.

Martin Steiniger, a computational biologist at Seoul National University, says his biggest question is how well ESMFold2 can predict the structure of proteins that are so different from those already known. His team found that the first version of ESMFold was not particularly good at predicting unusual protein structures, especially those found in metagenome data.

Sergei Ovchinnikov, a computational biologist at the Massachusetts Institute of Technology in Cambridge, sees the ESM atlas as a complement to, rather than a replacement for, the widely used AlphaFold database of more than 200 million protein structures.

ESMFold2’s predictions of interacting proteins are impressive, but not so surprising, says Ovchinnikov. Earlier this year, Google DeepMind Biopharma spun off Isomorphic Labs Unveiled a proprietary model Which brought substantial benefits in predicting such structures. Open-source models, which the Biohub team has not compared directly to ESMfold2, have also achieved impressive results in predicting protein interactions, Ovchinnikov says.

The completely open-source nature of ESMFold2, with no restrictions on commercial use, means it could have widespread use, says Ovchinnikov. “I hope many people will be excited to try ESMFold2.”

This article is reproduced with permission and was first published On 27th May 2026.

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