Mapping the metagenomic protein universe
ESM Metagenomic Atlas (esmatlas.com) scores 63 out of 100 on Brand Analyzer, placing it in the Developing tier among Technology brands, Education brands, Healthcare brands, in the 10th percentile of 28223 Technology brands.
ESM Metagenomic Atlas is an open-access online database containing 617 million predicted protein structures derived from metagenomic sequencing data. Structures are generated using Meta AI's ESM protein language models, which predict 3D structure from amino acid sequence. The resource is designed to help researchers explore previously uncharacterized proteins at large scale, supporting studies in biology, bioinformatics, and metagenomics. It is positioned as an open, AI-driven complement to existing structural biology resources, aimed at accelerating scientific discovery in protein structure research.
| Brand Name | ESM Metagenomic Atlas |
|---|---|
| Domain | esmatlas.com |
| Industry | Technology, Education |
| Parent Company / Owner | Meta AI |
| Main Competitors | AlphaFold Protein Structure Database, Protein Data Bank (RCSB PDB) (88/100), UniProt (92/100), InterPro (64/100), NCBI |
Moz Domain Authority 36/100 vs category average 40 / leader 100 - limited third-party links, so AI systems rarely encounter mentions of the brand.
No Wikipedia presence was found - a major gap, since Wikipedia is among the most heavily weighted sources in AI training data.
The homepage meta description reads "ESM Metagenomic Atlas. An open atlas of 617 million predicted metagenomic protein structures" (Meta description: 13 words (ideal length). OG description present and descriptive) - this is the summary AI engines are most likely to quote.
Structured data on the homepage: og:title, og:description, og:image, og:type, Twitter cards. Adding Organization and FAQ schema would further help AI crawlers parse the brand's identity.
AI-crawler access: robots.txt present, no AI bot restrictions - robots.txt controls whether engines like GPTBot and ClaudeBot can read the site at all.
Social footprint: verified profiles on GitHub, Instagram; no detected presence on Facebook, X (Twitter), LinkedIn, YouTube - consistent profiles reinforce the brand's identity across the web.
0 Reddit mentions - community discussion signals real-world reputation to AI models.
Live results from asking a general-purpose AI assistant about the brand, checked September 2026.
When asked "What is ESM Metagenomic Atlas?", Claude could identify the brand as of September 2026. ESM Metagenomic Atlas is a database and web resource created by Meta AI that used the ESMFold protein language model to predict the 3D structures of hundreds of millions of proteins from metagenomic sequences, many of which had never been structurally characterized before. Best known for Using Meta's ESMFold language model to predict structures for over 600 million metagenomic proteins, offering a fast alternative to AlphaFold for exploring unch.
When asked "Best brands similar to ESM Metagenomic Atlas?", Claude would recommend ESM Metagenomic Atlas as of September 2026. It's a notable large-scale open resource for structural biology and would come up naturally alongside tools like AlphaFold DB when discussing protein structure prediction databases.
Common questions people ask in Google, ChatGPT, Claude, Gemini, Perplexity, and other AI search engines.
ESM Metagenomic Atlas's main competitors in Technology: Protein Data Bank (RCSB PDB) (88/100), UniProt (92/100), InterPro (64/100).
Sources: Brand Analyzer scan
ESM Metagenomic Atlas is an open, online database of 617 million predicted metagenomic protein structures. It was created using Meta AI's ESM protein language models, which predict three-dimensional protein structures directly from amino acid sequences. The atlas focuses on metagenomic proteins-those derived from environmental DNA samples rather than a single organism-many of which have no previously known structure. It is intended as a resource for biologists, computational researchers, and bioinformaticians to explore the structural diversity of proteins found across ecosystems.
Sources: ESM Metagenomic Atlas official site
ESM Metagenomic Atlas has low AI-search visibility, scoring 17/100 on Brand Analyzer's AI visibility composite (visibility 0, trust 19, recommendation 45). This estimates how likely AI engines like ChatGPT, Claude, Gemini and Perplexity are to know, trust, and recommend the brand.
Sources: ESM Metagenomic Atlas official site
ESM Metagenomic Atlas offers a single core resource: an open, searchable database of 617 million predicted metagenomic protein structures. Users can access and explore these AI-generated structural predictions, which are produced using Meta AI's ESM protein language models, for research and scientific discovery purposes.
Sources: ESM Metagenomic Atlas official site
ESM Metagenomic Atlas scores 45/100 on Brand Analyzer's AI recommendation signal, indicating it is less likely to be surfaced when AI assistants like ChatGPT suggest Technology options. Recommendation depends on crawlability, structured data, and category authority.
Sources: ESM Metagenomic Atlas official site
ESM Metagenomic Atlas can improve AI discoverability by strengthening structured data (Organization and FAQ schema), maintaining an accurate Wikipedia/Wikidata entity, earning authoritative citations, and keeping content crawlable for AI bots. Brand Analyzer measures these as visibility, trust, and recommendation signals.
Sources: ESM Metagenomic Atlas official site
ESM Metagenomic Atlas is used by biologists, computational researchers, bioinformaticians, and scientific institutions engaged in studying protein structures and metagenomics. These users rely on the atlas to explore predicted structures of proteins that lack experimentally determined structures, supporting research into protein function, evolution, and environmental biodiversity.
Sources: ESM Metagenomic Atlas official site
ESM Metagenomic Atlas is known for being one of the largest open collections of predicted protein structures, covering 617 million metagenomic proteins. It is notable for using Meta AI's ESM protein language models to generate structural predictions at a scale far larger than earlier structural databases, expanding coverage into previously uncharacterized regions of protein space derived from environmental metagenomic data.
Sources: ESM Metagenomic Atlas official site
ESM Metagenomic Atlas scores 17/100 for AI visibility - how well ChatGPT, Claude, Perplexity and Google AI Overviews can discover, identify and cite the brand.
ESM Metagenomic Atlas has a weak AI-visibility profile at 17/100 - a proxy for how readily ChatGPT, Perplexity and Google's AI Overviews can recognise and cite it. Its strongest area is recommendation likelihood (45/100) and its weakest is visibility (0/100). It benefits from open access for AI crawlers (GPTBot, ClaudeBot, etc.). The main gaps holding it back: no clear Wikipedia entity to anchor it in LLM training data, no strong Wikidata entry and a low Moz Domain Authority of 36/100. In a live check, Claude could already identify ESM Metagenomic Atlas from memory (September 2026) - a sign these signals are paying off.
The score combines seven dimensions - name quality, digital presence, visual identity, messaging clarity, trust foundation, AI discoverability, and brand authority - into a single objective benchmark.
Ranked closest to ESM Metagenomic Atlas: Empathy Lab (63/100), Email Unsubscriber (63/100), eTrac Technologies (63/100), Evenplay (63/100).
A step up - brands to learn from: Zocket (71/100), Zhangyue (71/100).
Category leader: Hostinger (97/100).
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