Biohub releases a world model of protein biology

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May 27, 2026

Biohub releases a world model of protein biology

Biohub’s open models map the protein universe and design functional binders with therapeutic-level affinity in the lab.

AI, Data, Technology

REDWOOD CITY, Calif., May 27, 2026 – Biohub today announced the release of a world model of protein biology: a scientific engine for prediction, design, and discovery that can map proteins across the tree of life, predict their structures, and design new protein binders that function in laboratory experiments.

Proteins are the machinery of life. Nearly every function of the human body depends on them. They are among the most important targets in medicine, yet designing functional, stable proteins that work as intended in the body is an immense scientific and technical challenge.

Today, Biohub is making available to researchers everywhere an open discovery engine for protein structure prediction, design, and biological discovery built around three releases: ESMC, ESMFold2, and ESM Atlas:

The core scientific hypothesis of ESM is that training a language model across the sequences of all life will cause it to internalize the fundamental properties that govern protein biology — the rules underlying how proteins fold, interact, and function across all of life. At its foundation is ESMC , a state-of-the-art language model that represents proteins, trained on approximately 2.8 billion sequences drawn from across all of life.

ESMFold2 is the design engine built to transform ESMC’s sequence representations into atomically-resolved 3D structure of biomolecular complexes. In experiments described in a preprint posted today, researchers used ESMFold2 to design protein binders against five targets central to cancer and immunology — a computational search completed in days, rather than several months or years. The lab-validated binders exhibited high affinity, specificity, and stability — properties critical for clinical utility — and showed minimal similarity to sequences in public databases, suggesting the model is producing de novo solutions, rather than retrieving known binders.

ESM Atlas makes ESMC’s representations navigable across 6.8 billion protein sequences and 1.1 billion predicted structures — the largest application of AI to protein biology to date. It organizes proteins by relationships the model has learned, surfacing connections that existing databases have not captured, including evolutionary links between gene-editing enzymes spread across distant branches of life. Much of that biology has never been annotated. For researchers working on diseases where the biology is poorly understood, it makes uncharacterized biology searchable.

All three are freely available to the global scientific community at Biohub Platform.

“Designing the interactions between proteins is a fundamental problem in biochemistry, and critical for the design of medicines. What we’ve shown is that these models have learned such a high-fidelity world model of biology that you can design protein interfaces computationally, take them into the laboratory, and they function as predicted.”

— Alex Rives, Head of Science, Biohub

ESMFold2: A faster path from protein biology to binder design

ESMFold2 is an open, state-of-the-art structure prediction model that translates knowledge of patterns across evolution encoded in ESMC into precise, atomic-resolution 3D models of proteins and their interactions. It leads across standard protein folding benchmarks at predicting protein-protein and antibody-antigen interactions.

Antibody-based therapies have become a cornerstone of modern medicine, accounting for roughly one quarter of all new FDA drug approvals, spanning cancers, autoimmune diseases, and conditions that once had few treatment options. Finding a viable therapeutic candidate depends on identifying molecules that bind tightly and specifically to a disease target; a single preclinical binder candidate typically takes three to four years to develop. ESMFold2, which predicts the structural configurations most likely to achieve high affinity for a given target, can move much of the initial search into computation, producing experimentally testable designs in days.

Biohub researchers used the model to design protein binders against five targets at the center of cancer and immunology research — EGFR and PDGFRβ (implicated in tumor growth), PD-L1 and CTLA-4 (immune checkpoints that cancer cells exploit to evade detection), and CD45 (a regulator of immune cell signaling). Designs achieved hit rates of 36–88% for compact minibinders and 15–29% for antibody-derived formats, with confirmed binding in laboratory experiments. For PD-L1, designed binders restored T cell signaling in laboratory tests, blocking the same pathway that approved checkpoint therapies target.

ESMFold2 changes the accuracy and speed of early therapeutic binder discovery, transforming...