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ESM Atlas Review

ESM Metagenomic Atlas is an open atlas of billions of predicted metagenomic protein structures, enabling biological discovery and protein design.

shipped Jun 1, 2026researchfreemium
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ESM Atlas - AI tool

Why it matters

1ESM Atlas provides access to 617 million predicted metagenomic protein structures.
2The database contains 1.1 billion predicted protein structures and 6.8 billion protein sequence entries.
3Launched on May 27, 2026, by the biomedical research institute Biohub.
4Structures were generated by the AI model ESMFold2, which is fully open-source and allows unrestricted commercial use.

Stork’s verdict on ESM Atlas

ESM Atlas offers billions of open metagenomic protein structures, but its specialized focus demands deep domain expertise.

ESM Atlas reviewed by Stork AI · stork.ai/en/esm-atlas

About ESM Atlas

Platforms
Web
Target Audience
Researchers and developers in metagenomics and bioinformatics

Leadership

Meta AI

Specs

API Docs

View Documentation →

API Available

Yes, public API

overview

What is ESM Atlas?

ESM Atlas is a structural biology AI tool developed by Biohub (founded by Mark Zuckerberg) that enables researchers in metagenomics and structural biology to access and explore predicted metagenomic protein structures. It provides access to 617 million predicted metagenomic protein structures and was generated by the AI model ESMFold2. This open atlas serves as a critical resource for understanding protein function, facilitating drug discovery, and exploring uncharacterized biological diversity. The underlying ESMFold2 model predicts 3D protein structures directly from amino acid sequences and was released alongside ESMC, a state-of-the-art protein language model.

features

Key Features of ESM Atlas

ESM Atlas offers a robust set of features designed to support advanced research in structural biology and metagenomics. Its core functionality revolves around providing extensive access to predicted protein structures and the tools to leverage this data.

  • Access to 617 million predicted metagenomic protein structures.
  • Comprehensive database featuring 1.1 billion predicted protein structures and 6.8 billion protein sequence entries.
  • Structures generated by the advanced AI model ESMFold2, which predicts 3D protein structures from amino acid sequences.
  • ESMFold2 is fully open-source and allows unrestricted commercial use, promoting broad scientific application.
  • An API is available at https://esmatlas.com/about#api for programmatic access to the database, subject to rate limits.
  • Organizes proteins by relationships learned by the model, revealing novel connections not found in traditional databases.
  • Supports protein design capabilities, enabling researchers to design and test new functional proteins.
  • Incorporates a large amount of microbial protein data, offering insights into diverse environmental biology.
  • User-friendly interface designed for researchers in various biological fields.

use cases

Who Should Use ESM Atlas?

ESM Atlas is primarily designed for the scientific community, providing a foundational resource for various research and development activities in biology and medicine.

  • Biologists: For understanding protein function, exploring uncharacterized biology, and inferring roles of proteins in biological processes.
  • Bioinformaticians: For advanced bioinformatics applications, data analysis in genomics, and integrating large-scale structural data into computational workflows.
  • Structural Biologists: For protein structure prediction, exploring metagenomic protein diversity, and analyzing protein shapes to inform experimental design.
  • Researchers in protein science: For biological discovery, protein design, and accelerating early therapeutic binder discovery against targets in cancer and immunology.
  • Disease researchers: For designing new drugs and therapeutics, and exploring protein connections relevant to poorly understood diseases.

pricing

ESM Atlas Pricing & Plans

ESM Atlas operates on a freemium model, providing open access to its comprehensive database of predicted metagenomic protein structures. The underlying ESMFold2 model is fully open-source and permits unrestricted commercial use, a significant departure from some proprietary models. Access to the API is available, though it is subject to rate limits on sequence length and the number of requests per user to ensure fair usage as a shared resource. Specific paid tiers for increased API capacity or enterprise features are not publicly detailed, but the core resource is freely accessible for research and development.

  • Open Access: Free access to the 617 million predicted metagenomic protein structures via the web interface.
  • API Access: Free with rate limits on sequence length and number of requests per user, as it is a shared resource.
  • ESMFold2 Model: Fully open-source and available for unrestricted commercial use, allowing local deployment and custom applications.

Similar Tools

ESM Atlas vs Competitors

ESM Atlas and its underlying ESMFold2 model are positioned as a significant advancement in structural biology, directly challenging existing solutions with its scale, performance, and open-source nature.

1
AlphaFold Protein Structure Database↗

Offers a vast, highly accurate database of over 200 million predicted protein structures, covering nearly all catalogued proteins known to science.

Similar to ESM Atlas in providing a large, open-access database of predicted protein structures for research. While ESM Atlas specifically focuses on metagenomic proteins and emphasizes speed with its language model, AlphaFold is renowned for its high accuracy across a broader range of proteins and has a larger overall database size, though not exclusively metagenomic.

2
RoseTTAFold (Baker Lab)↗

Integrates deep learning with traditional energy-based methods to predict tertiary protein structures and protein-protein interactions, including complete biological assemblies.

Unlike ESM Atlas, which is a pre-computed atlas of metagenomic structures, RoseTTAFold is a powerful AI prediction tool that researchers use to generate structures on demand, including protein complexes, rather than browsing a pre-existing database.

3
OpenProtein.AI↗

Provides a no-code platform with powerful foundation models for protein engineering, structure/function prediction, and model training, making advanced AI accessible to biologists.

While ESM Atlas is a static atlas of predicted structures, OpenProtein.AI offers an interactive platform for designing and predicting new proteins using AI, including custom model training. It targets researchers but focuses on active protein engineering rather than just providing access to a pre-computed database, and offers a free tier for academia.

4
OmegaFoldOn Stork

A single-sequence based model that excels at predicting structures for orphan proteins and in antibody design without requiring multiple sequence alignments (MSAs), offering a balance between speed and accuracy.

Similar to ESMFold (the underlying model for ESM Atlas) in being a single-sequence based prediction tool, OmegaFold offers an alternative for researchers needing fast predictions, especially for proteins lacking evolutionary information. Unlike the pre-computed ESM Atlas, OmegaFold is a tool for on-demand prediction, often used for novel or de novo designed proteins.

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